diff --git a/README_GAUDI.md b/README_GAUDI.md
index 04e2ff22f96e5..6ba3bb50d4a04 100644
--- a/README_GAUDI.md
+++ b/README_GAUDI.md
@@ -195,10 +195,10 @@ batch size and sequence length dimension. These parameters can be
 observed in logs during vLLM startup:
 
 ``` {.}
-INFO 08-01 21:37:59 habana_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
-INFO 08-01 21:37:59 habana_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
-INFO 08-01 21:37:59 habana_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
-INFO 08-01 21:37:59 habana_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
+INFO 08-01 21:37:59 hpu_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
+INFO 08-01 21:37:59 hpu_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
+INFO 08-01 21:37:59 hpu_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
+INFO 08-01 21:37:59 hpu_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
 ```
 
 `min` determines the lowest value of the bucket. `step` determines the
@@ -267,17 +267,17 @@ graph compilation overheads within bucket boundaries during server
 runtime. Each warmup step is logged during vLLM startup:
 
 ``` {.}
-INFO 08-01 22:26:47 habana_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:79.16 GiB
-INFO 08-01 22:26:47 habana_model_runner.py:1066] [Warmup][Prompt][2/24] batch_size:4 seq_len:896 free_mem:55.43 GiB
-INFO 08-01 22:26:48 habana_model_runner.py:1066] [Warmup][Prompt][3/24] batch_size:4 seq_len:768 free_mem:55.43 GiB
+INFO 08-01 22:26:47 hpu_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:79.16 GiB
+INFO 08-01 22:26:47 hpu_model_runner.py:1066] [Warmup][Prompt][2/24] batch_size:4 seq_len:896 free_mem:55.43 GiB
+INFO 08-01 22:26:48 hpu_model_runner.py:1066] [Warmup][Prompt][3/24] batch_size:4 seq_len:768 free_mem:55.43 GiB
 ...
-INFO 08-01 22:26:59 habana_model_runner.py:1066] [Warmup][Prompt][24/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
-INFO 08-01 22:27:00 habana_model_runner.py:1066] [Warmup][Decode][1/48] batch_size:4 seq_len:2048 free_mem:55.43 GiB
-INFO 08-01 22:27:00 habana_model_runner.py:1066] [Warmup][Decode][2/48] batch_size:4 seq_len:1920 free_mem:55.43 GiB
-INFO 08-01 22:27:01 habana_model_runner.py:1066] [Warmup][Decode][3/48] batch_size:4 seq_len:1792 free_mem:55.43 GiB
+INFO 08-01 22:26:59 hpu_model_runner.py:1066] [Warmup][Prompt][24/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
+INFO 08-01 22:27:00 hpu_model_runner.py:1066] [Warmup][Decode][1/48] batch_size:4 seq_len:2048 free_mem:55.43 GiB
+INFO 08-01 22:27:00 hpu_model_runner.py:1066] [Warmup][Decode][2/48] batch_size:4 seq_len:1920 free_mem:55.43 GiB
+INFO 08-01 22:27:01 hpu_model_runner.py:1066] [Warmup][Decode][3/48] batch_size:4 seq_len:1792 free_mem:55.43 GiB
 ...
-INFO 08-01 22:27:16 habana_model_runner.py:1066] [Warmup][Decode][47/48] batch_size:2 seq_len:128 free_mem:55.43 GiB
-INFO 08-01 22:27:16 habana_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
+INFO 08-01 22:27:16 hpu_model_runner.py:1066] [Warmup][Decode][47/48] batch_size:2 seq_len:128 free_mem:55.43 GiB
+INFO 08-01 22:27:16 hpu_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
 ```
 
 This example uses the same buckets as in *Bucketing mechanism* section.
@@ -374,35 +374,35 @@ Each described step is logged by vLLM server, as follows (negative
 values correspond to memory being released):
 
 ``` {.}
-INFO 08-02 17:37:44 habana_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
-INFO 08-02 17:37:44 habana_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
-INFO 08-02 17:37:44 habana_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
-INFO 08-02 17:37:44 habana_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
-INFO 08-02 17:37:52 habana_model_runner.py:430] Pre-loading model weights on hpu:0 took 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
-INFO 08-02 17:37:52 habana_model_runner.py:438] Wrapping in HPU Graph took 0 B of device memory (14.97 GiB/94.62 GiB used) and -252 KiB of host memory (475.2 GiB/1007 GiB used)
-INFO 08-02 17:37:52 habana_model_runner.py:442] Loading model weights took in total 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
-INFO 08-02 17:37:54 habana_worker.py:134] Model profiling run took 504 MiB of device memory (15.46 GiB/94.62 GiB used) and 180.9 MiB of host memory (475.4 GiB/1007 GiB used)
-INFO 08-02 17:37:54 habana_worker.py:158] Free device memory: 79.16 GiB, 39.58 GiB usable (gpu_memory_utilization=0.5), 15.83 GiB reserved for HPUGraphs (VLLM_GRAPH_RESERVED_MEM=0.4), 23.75 GiB reserved for KV cache
-INFO 08-02 17:37:54 habana_executor.py:85] # HPU blocks: 1519, # CPU blocks: 0
-INFO 08-02 17:37:54 habana_worker.py:190] Initializing cache engine took 23.73 GiB of device memory (39.2 GiB/94.62 GiB used) and -1.238 MiB of host memory (475.4 GiB/1007 GiB used)
-INFO 08-02 17:37:54 habana_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:55.43 GiB
+INFO 08-02 17:37:44 hpu_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
+INFO 08-02 17:37:44 hpu_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
+INFO 08-02 17:37:44 hpu_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
+INFO 08-02 17:37:44 hpu_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
+INFO 08-02 17:37:52 hpu_model_runner.py:430] Pre-loading model weights on hpu:0 took 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
+INFO 08-02 17:37:52 hpu_model_runner.py:438] Wrapping in HPU Graph took 0 B of device memory (14.97 GiB/94.62 GiB used) and -252 KiB of host memory (475.2 GiB/1007 GiB used)
+INFO 08-02 17:37:52 hpu_model_runner.py:442] Loading model weights took in total 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
+INFO 08-02 17:37:54 hpu_worker.py:134] Model profiling run took 504 MiB of device memory (15.46 GiB/94.62 GiB used) and 180.9 MiB of host memory (475.4 GiB/1007 GiB used)
+INFO 08-02 17:37:54 hpu_worker.py:158] Free device memory: 79.16 GiB, 39.58 GiB usable (gpu_memory_utilization=0.5), 15.83 GiB reserved for HPUGraphs (VLLM_GRAPH_RESERVED_MEM=0.4), 23.75 GiB reserved for KV cache
+INFO 08-02 17:37:54 hpu_executor.py:85] # HPU blocks: 1519, # CPU blocks: 0
+INFO 08-02 17:37:54 hpu_worker.py:190] Initializing cache engine took 23.73 GiB of device memory (39.2 GiB/94.62 GiB used) and -1.238 MiB of host memory (475.4 GiB/1007 GiB used)
+INFO 08-02 17:37:54 hpu_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:55.43 GiB
 ...
-INFO 08-02 17:38:22 habana_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
-INFO 08-02 17:38:22 habana_model_runner.py:1159] Using 15.85 GiB/55.43 GiB of free device memory for HPUGraphs, 4.755 GiB for prompt and 11.095 GiB for decode (VLLM_GRAPH_PROMPT_RATIO=0.3)
-INFO 08-02 17:38:22 habana_model_runner.py:1066] [Warmup][Graph/Prompt][1/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
+INFO 08-02 17:38:22 hpu_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
+INFO 08-02 17:38:22 hpu_model_runner.py:1159] Using 15.85 GiB/55.43 GiB of free device memory for HPUGraphs, 4.755 GiB for prompt and 11.095 GiB for decode (VLLM_GRAPH_PROMPT_RATIO=0.3)
+INFO 08-02 17:38:22 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][1/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
 ...
-INFO 08-02 17:38:26 habana_model_runner.py:1066] [Warmup][Graph/Prompt][11/24] batch_size:1 seq_len:896 free_mem:48.77 GiB
-INFO 08-02 17:38:27 habana_model_runner.py:1066] [Warmup][Graph/Decode][1/48] batch_size:4 seq_len:128 free_mem:47.51 GiB
+INFO 08-02 17:38:26 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][11/24] batch_size:1 seq_len:896 free_mem:48.77 GiB
+INFO 08-02 17:38:27 hpu_model_runner.py:1066] [Warmup][Graph/Decode][1/48] batch_size:4 seq_len:128 free_mem:47.51 GiB
 ...
-INFO 08-02 17:38:41 habana_model_runner.py:1066] [Warmup][Graph/Decode][48/48] batch_size:1 seq_len:2048 free_mem:47.35 GiB
-INFO 08-02 17:38:41 habana_model_runner.py:1066] [Warmup][Graph/Prompt][12/24] batch_size:4 seq_len:256 free_mem:47.35 GiB
-INFO 08-02 17:38:42 habana_model_runner.py:1066] [Warmup][Graph/Prompt][13/24] batch_size:2 seq_len:512 free_mem:45.91 GiB
-INFO 08-02 17:38:42 habana_model_runner.py:1066] [Warmup][Graph/Prompt][14/24] batch_size:1 seq_len:1024 free_mem:44.48 GiB
-INFO 08-02 17:38:43 habana_model_runner.py:1066] [Warmup][Graph/Prompt][15/24] batch_size:2 seq_len:640 free_mem:43.03 GiB
-INFO 08-02 17:38:43 habana_model_runner.py:1128] Graph/Prompt captured:15 (62.5%) used_mem:14.03 GiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (4, 128), (4, 256)]
-INFO 08-02 17:38:43 habana_model_runner.py:1128] Graph/Decode captured:48 (100.0%) used_mem:161.9 MiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
-INFO 08-02 17:38:43 habana_model_runner.py:1206] Warmup finished in 49 secs, allocated 14.19 GiB of device memory
-INFO 08-02 17:38:43 habana_executor.py:91] init_cache_engine took 37.92 GiB of device memory (53.39 GiB/94.62 GiB used) and 57.86 MiB of host memory (475.4 GiB/1007 GiB used)
+INFO 08-02 17:38:41 hpu_model_runner.py:1066] [Warmup][Graph/Decode][48/48] batch_size:1 seq_len:2048 free_mem:47.35 GiB
+INFO 08-02 17:38:41 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][12/24] batch_size:4 seq_len:256 free_mem:47.35 GiB
+INFO 08-02 17:38:42 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][13/24] batch_size:2 seq_len:512 free_mem:45.91 GiB
+INFO 08-02 17:38:42 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][14/24] batch_size:1 seq_len:1024 free_mem:44.48 GiB
+INFO 08-02 17:38:43 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][15/24] batch_size:2 seq_len:640 free_mem:43.03 GiB
+INFO 08-02 17:38:43 hpu_model_runner.py:1128] Graph/Prompt captured:15 (62.5%) used_mem:14.03 GiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (4, 128), (4, 256)]
+INFO 08-02 17:38:43 hpu_model_runner.py:1128] Graph/Decode captured:48 (100.0%) used_mem:161.9 MiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
+INFO 08-02 17:38:43 hpu_model_runner.py:1206] Warmup finished in 49 secs, allocated 14.19 GiB of device memory
+INFO 08-02 17:38:43 hpu_executor.py:91] init_cache_engine took 37.92 GiB of device memory (53.39 GiB/94.62 GiB used) and 57.86 MiB of host memory (475.4 GiB/1007 GiB used)
 ```
 
 Recommended vLLM Parameters
diff --git a/docs/source/getting_started/gaudi-installation.rst b/docs/source/getting_started/gaudi-installation.rst
index db1d8666e4800..5915de92802d9 100644
--- a/docs/source/getting_started/gaudi-installation.rst
+++ b/docs/source/getting_started/gaudi-installation.rst
@@ -173,10 +173,10 @@ Bucketing ranges are determined with 3 parameters - ``min``, ``step`` and ``max`
 
 .. code-block::
 
-      INFO 08-01 21:37:59 habana_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
-      INFO 08-01 21:37:59 habana_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
-      INFO 08-01 21:37:59 habana_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
-      INFO 08-01 21:37:59 habana_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
+      INFO 08-01 21:37:59 hpu_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
+      INFO 08-01 21:37:59 hpu_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
+      INFO 08-01 21:37:59 hpu_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
+      INFO 08-01 21:37:59 hpu_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
 
 ``min`` determines the lowest value of the bucket. ``step`` determines the interval between buckets, and ``max`` determines the upper bound of the bucket. Furthermore, interval between ``min`` and ``step`` has special handling - ``min`` gets multiplied by consecutive powers of two, until ``step`` gets reached. We call this the ramp-up phase and it is used for handling lower batch sizes with minimum wastage, while allowing larger padding on larger batch sizes.
 
@@ -216,17 +216,17 @@ Warmup is an optional, but highly recommended step occurring before vLLM server
 
 .. code-block::
 
-   INFO 08-01 22:26:47 habana_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:79.16 GiB
-   INFO 08-01 22:26:47 habana_model_runner.py:1066] [Warmup][Prompt][2/24] batch_size:4 seq_len:896 free_mem:55.43 GiB
-   INFO 08-01 22:26:48 habana_model_runner.py:1066] [Warmup][Prompt][3/24] batch_size:4 seq_len:768 free_mem:55.43 GiB
+   INFO 08-01 22:26:47 hpu_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:79.16 GiB
+   INFO 08-01 22:26:47 hpu_model_runner.py:1066] [Warmup][Prompt][2/24] batch_size:4 seq_len:896 free_mem:55.43 GiB
+   INFO 08-01 22:26:48 hpu_model_runner.py:1066] [Warmup][Prompt][3/24] batch_size:4 seq_len:768 free_mem:55.43 GiB
    ...
-   INFO 08-01 22:26:59 habana_model_runner.py:1066] [Warmup][Prompt][24/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
-   INFO 08-01 22:27:00 habana_model_runner.py:1066] [Warmup][Decode][1/48] batch_size:4 seq_len:2048 free_mem:55.43 GiB
-   INFO 08-01 22:27:00 habana_model_runner.py:1066] [Warmup][Decode][2/48] batch_size:4 seq_len:1920 free_mem:55.43 GiB
-   INFO 08-01 22:27:01 habana_model_runner.py:1066] [Warmup][Decode][3/48] batch_size:4 seq_len:1792 free_mem:55.43 GiB
+   INFO 08-01 22:26:59 hpu_model_runner.py:1066] [Warmup][Prompt][24/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
+   INFO 08-01 22:27:00 hpu_model_runner.py:1066] [Warmup][Decode][1/48] batch_size:4 seq_len:2048 free_mem:55.43 GiB
+   INFO 08-01 22:27:00 hpu_model_runner.py:1066] [Warmup][Decode][2/48] batch_size:4 seq_len:1920 free_mem:55.43 GiB
+   INFO 08-01 22:27:01 hpu_model_runner.py:1066] [Warmup][Decode][3/48] batch_size:4 seq_len:1792 free_mem:55.43 GiB
    ...
-   INFO 08-01 22:27:16 habana_model_runner.py:1066] [Warmup][Decode][47/48] batch_size:2 seq_len:128 free_mem:55.43 GiB
-   INFO 08-01 22:27:16 habana_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
+   INFO 08-01 22:27:16 hpu_model_runner.py:1066] [Warmup][Decode][47/48] batch_size:2 seq_len:128 free_mem:55.43 GiB
+   INFO 08-01 22:27:16 hpu_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
 
 This example uses the same buckets as in *Bucketing mechanism* section. Each output line corresponds to execution of a single bucket. When bucket is executed for the first time, its graph is compiled and can be reused later on, skipping further graph compilations. 
 
@@ -266,35 +266,35 @@ Each described step is logged by vLLM server, as follows (negative values corres
 
 .. code-block::
 
-   INFO 08-02 17:37:44 habana_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
-   INFO 08-02 17:37:44 habana_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
-   INFO 08-02 17:37:44 habana_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
-   INFO 08-02 17:37:44 habana_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
-   INFO 08-02 17:37:52 habana_model_runner.py:430] Pre-loading model weights on hpu:0 took 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
-   INFO 08-02 17:37:52 habana_model_runner.py:438] Wrapping in HPU Graph took 0 B of device memory (14.97 GiB/94.62 GiB used) and -252 KiB of host memory (475.2 GiB/1007 GiB used)
-   INFO 08-02 17:37:52 habana_model_runner.py:442] Loading model weights took in total 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
-   INFO 08-02 17:37:54 habana_worker.py:134] Model profiling run took 504 MiB of device memory (15.46 GiB/94.62 GiB used) and 180.9 MiB of host memory (475.4 GiB/1007 GiB used)
-   INFO 08-02 17:37:54 habana_worker.py:158] Free device memory: 79.16 GiB, 39.58 GiB usable (gpu_memory_utilization=0.5), 15.83 GiB reserved for HPUGraphs (VLLM_GRAPH_RESERVED_MEM=0.4), 23.75 GiB reserved for KV cache
-   INFO 08-02 17:37:54 habana_executor.py:85] # HPU blocks: 1519, # CPU blocks: 0
-   INFO 08-02 17:37:54 habana_worker.py:190] Initializing cache engine took 23.73 GiB of device memory (39.2 GiB/94.62 GiB used) and -1.238 MiB of host memory (475.4 GiB/1007 GiB used)
-   INFO 08-02 17:37:54 habana_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:55.43 GiB
+   INFO 08-02 17:37:44 hpu_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
+   INFO 08-02 17:37:44 hpu_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]
+   INFO 08-02 17:37:44 hpu_model_runner.py:504] Decode bucket config (min, step, max_warmup) bs:[1, 128, 4], seq:[128, 128, 2048]
+   INFO 08-02 17:37:44 hpu_model_runner.py:509] Generated 48 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
+   INFO 08-02 17:37:52 hpu_model_runner.py:430] Pre-loading model weights on hpu:0 took 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
+   INFO 08-02 17:37:52 hpu_model_runner.py:438] Wrapping in HPU Graph took 0 B of device memory (14.97 GiB/94.62 GiB used) and -252 KiB of host memory (475.2 GiB/1007 GiB used)
+   INFO 08-02 17:37:52 hpu_model_runner.py:442] Loading model weights took in total 14.97 GiB of device memory (14.97 GiB/94.62 GiB used) and 2.95 GiB of host memory (475.2 GiB/1007 GiB used)
+   INFO 08-02 17:37:54 hpu_worker.py:134] Model profiling run took 504 MiB of device memory (15.46 GiB/94.62 GiB used) and 180.9 MiB of host memory (475.4 GiB/1007 GiB used)
+   INFO 08-02 17:37:54 hpu_worker.py:158] Free device memory: 79.16 GiB, 39.58 GiB usable (gpu_memory_utilization=0.5), 15.83 GiB reserved for HPUGraphs (VLLM_GRAPH_RESERVED_MEM=0.4), 23.75 GiB reserved for KV cache
+   INFO 08-02 17:37:54 hpu_executor.py:85] # HPU blocks: 1519, # CPU blocks: 0
+   INFO 08-02 17:37:54 hpu_worker.py:190] Initializing cache engine took 23.73 GiB of device memory (39.2 GiB/94.62 GiB used) and -1.238 MiB of host memory (475.4 GiB/1007 GiB used)
+   INFO 08-02 17:37:54 hpu_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:55.43 GiB
    ...
-   INFO 08-02 17:38:22 habana_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
-   INFO 08-02 17:38:22 habana_model_runner.py:1159] Using 15.85 GiB/55.43 GiB of free device memory for HPUGraphs, 7.923 GiB for prompt and 7.923 GiB for decode (VLLM_GRAPH_PROMPT_RATIO=0.3)
-   INFO 08-02 17:38:22 habana_model_runner.py:1066] [Warmup][Graph/Prompt][1/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
+   INFO 08-02 17:38:22 hpu_model_runner.py:1066] [Warmup][Decode][48/48] batch_size:1 seq_len:128 free_mem:55.43 GiB
+   INFO 08-02 17:38:22 hpu_model_runner.py:1159] Using 15.85 GiB/55.43 GiB of free device memory for HPUGraphs, 7.923 GiB for prompt and 7.923 GiB for decode (VLLM_GRAPH_PROMPT_RATIO=0.3)
+   INFO 08-02 17:38:22 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][1/24] batch_size:1 seq_len:128 free_mem:55.43 GiB
    ...
-   INFO 08-02 17:38:26 habana_model_runner.py:1066] [Warmup][Graph/Prompt][11/24] batch_size:1 seq_len:896 free_mem:48.77 GiB
-   INFO 08-02 17:38:27 habana_model_runner.py:1066] [Warmup][Graph/Decode][1/48] batch_size:4 seq_len:128 free_mem:47.51 GiB
+   INFO 08-02 17:38:26 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][11/24] batch_size:1 seq_len:896 free_mem:48.77 GiB
+   INFO 08-02 17:38:27 hpu_model_runner.py:1066] [Warmup][Graph/Decode][1/48] batch_size:4 seq_len:128 free_mem:47.51 GiB
    ...
-   INFO 08-02 17:38:41 habana_model_runner.py:1066] [Warmup][Graph/Decode][48/48] batch_size:1 seq_len:2048 free_mem:47.35 GiB
-   INFO 08-02 17:38:41 habana_model_runner.py:1066] [Warmup][Graph/Prompt][12/24] batch_size:4 seq_len:256 free_mem:47.35 GiB
-   INFO 08-02 17:38:42 habana_model_runner.py:1066] [Warmup][Graph/Prompt][13/24] batch_size:2 seq_len:512 free_mem:45.91 GiB
-   INFO 08-02 17:38:42 habana_model_runner.py:1066] [Warmup][Graph/Prompt][14/24] batch_size:1 seq_len:1024 free_mem:44.48 GiB
-   INFO 08-02 17:38:43 habana_model_runner.py:1066] [Warmup][Graph/Prompt][15/24] batch_size:2 seq_len:640 free_mem:43.03 GiB
-   INFO 08-02 17:38:43 habana_model_runner.py:1128] Graph/Prompt captured:15 (62.5%) used_mem:14.03 GiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (4, 128), (4, 256)]
-   INFO 08-02 17:38:43 habana_model_runner.py:1128] Graph/Decode captured:48 (100.0%) used_mem:161.9 MiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
-   INFO 08-02 17:38:43 habana_model_runner.py:1206] Warmup finished in 49 secs, allocated 14.19 GiB of device memory
-   INFO 08-02 17:38:43 habana_executor.py:91] init_cache_engine took 37.92 GiB of device memory (53.39 GiB/94.62 GiB used) and 57.86 MiB of host memory (475.4 GiB/1007 GiB used)
+   INFO 08-02 17:38:41 hpu_model_runner.py:1066] [Warmup][Graph/Decode][48/48] batch_size:1 seq_len:2048 free_mem:47.35 GiB
+   INFO 08-02 17:38:41 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][12/24] batch_size:4 seq_len:256 free_mem:47.35 GiB
+   INFO 08-02 17:38:42 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][13/24] batch_size:2 seq_len:512 free_mem:45.91 GiB
+   INFO 08-02 17:38:42 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][14/24] batch_size:1 seq_len:1024 free_mem:44.48 GiB
+   INFO 08-02 17:38:43 hpu_model_runner.py:1066] [Warmup][Graph/Prompt][15/24] batch_size:2 seq_len:640 free_mem:43.03 GiB
+   INFO 08-02 17:38:43 hpu_model_runner.py:1128] Graph/Prompt captured:15 (62.5%) used_mem:14.03 GiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (4, 128), (4, 256)]
+   INFO 08-02 17:38:43 hpu_model_runner.py:1128] Graph/Decode captured:48 (100.0%) used_mem:161.9 MiB buckets:[(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048)]
+   INFO 08-02 17:38:43 hpu_model_runner.py:1206] Warmup finished in 49 secs, allocated 14.19 GiB of device memory
+   INFO 08-02 17:38:43 hpu_executor.py:91] init_cache_engine took 37.92 GiB of device memory (53.39 GiB/94.62 GiB used) and 57.86 MiB of host memory (475.4 GiB/1007 GiB used)
 
 
 Recommended vLLM Parameters
diff --git a/vllm/engine/async_llm_engine.py b/vllm/engine/async_llm_engine.py
index cb489084f48de..6f3b73dbeee20 100644
--- a/vllm/engine/async_llm_engine.py
+++ b/vllm/engine/async_llm_engine.py
@@ -16,7 +16,7 @@
 from vllm.engine.metrics_types import StatLoggerBase
 from vllm.executor.executor_base import ExecutorAsyncBase
 from vllm.executor.gpu_executor import GPUExecutorAsync
-from vllm.executor.habana_executor import HabanaExecutorAsync
+from vllm.executor.hpu_executor import HPUExecutorAsync
 from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import PromptType
 from vllm.logger import init_logger
@@ -620,12 +620,11 @@ def _get_executor_cls(
         elif engine_config.device_config.device_type == "hpu":
             if distributed_executor_backend == "ray":
                 initialize_ray_cluster(engine_config.parallel_config)
-                from vllm.executor.ray_habana_executor import (
-                    RayHabanaExecutorAsync)
-                executor_class = RayHabanaExecutorAsync
+                from vllm.executor.ray_hpu_executor import RayHPUExecutorAsync
+                executor_class = RayHPUExecutorAsync
             else:
-                from vllm.executor.habana_executor import HabanaExecutorAsync
-                executor_class = HabanaExecutorAsync
+                from vllm.executor.hpu_executor import HPUExecutorAsync
+                executor_class = HPUExecutorAsync
         elif engine_config.device_config.device_type == "openvino":
             assert distributed_executor_backend is None, (
                 "Distributed execution is not supported with "
@@ -1206,7 +1205,7 @@ async def start_profile(self) -> None:
         # using type instead of isinstance to check to avoid capturing
         # inherited classes
         if type(self.engine.model_executor) == GPUExecutorAsync or \
-            type(self.engine.model_executor) == HabanaExecutorAsync:  # noqa: E721
+            type(self.engine.model_executor) == HPUExecutorAsync:  # noqa: E721
             self.engine.model_executor.start_profile()
         else:
             self.engine.model_executor._run_workers("start_profile")
@@ -1215,7 +1214,7 @@ async def stop_profile(self) -> None:
         # using type instead of isinstance to check to avoid capturing
         # inherited classes
         if type(self.engine.model_executor) == GPUExecutorAsync or \
-            type(self.engine.model_executor) == HabanaExecutorAsync:  # noqa: E721
+            type(self.engine.model_executor) == HPUExecutorAsync:  # noqa: E721
             self.engine.model_executor.stop_profile()
         else:
             self.engine.model_executor._run_workers("stop_profile")
diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py
index f41d074ad536c..3635443421e88 100644
--- a/vllm/engine/llm_engine.py
+++ b/vllm/engine/llm_engine.py
@@ -28,7 +28,7 @@
 from vllm.entrypoints.openai.logits_processors import get_logits_processors
 from vllm.executor.executor_base import ExecutorBase
 from vllm.executor.gpu_executor import GPUExecutor
-from vllm.executor.habana_executor import HabanaExecutor
+from vllm.executor.hpu_executor import HPUExecutor
 from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import (INPUT_REGISTRY, EncoderDecoderLLMInputs,
                          InputRegistry, LLMInputs, PromptType)
@@ -533,11 +533,11 @@ def _get_executor_cls(cls,
         elif engine_config.device_config.device_type == "hpu":
             if distributed_executor_backend == "ray":
                 initialize_ray_cluster(engine_config.parallel_config)
-                from vllm.executor.ray_habana_executor import RayHabanaExecutor
-                executor_class = RayHabanaExecutor
+                from vllm.executor.ray_hpu_executor import RayHPUExecutor
+                executor_class = RayHPUExecutor
             else:
-                from vllm.executor.habana_executor import HabanaExecutor
-                executor_class = HabanaExecutor
+                from vllm.executor.hpu_executor import HPUExecutor
+                executor_class = HPUExecutor
         elif engine_config.device_config.device_type == "openvino":
             from vllm.executor.openvino_executor import OpenVINOExecutor
             executor_class = OpenVINOExecutor
@@ -1796,7 +1796,7 @@ def start_profile(self) -> None:
         # using type instead of isinstance to check to avoid capturing
         # inherited classes (MultiprocessingGPUExecutor)
         if type(self.model_executor) == GPUExecutor or \
-            type(self.model_executor) == HabanaExecutor:  # noqa: E721
+            type(self.model_executor) == HPUExecutor:  # noqa: E721
             self.model_executor.start_profile()
         else:
             self.model_executor._run_workers("start_profile")
@@ -1805,7 +1805,7 @@ def stop_profile(self) -> None:
         # using type instead of isinstance to check to avoid capturing
         # inherited classes (MultiprocessingGPUExecutor)
         if type(self.model_executor) == GPUExecutor or \
-            type(self.model_executor) == HabanaExecutor:  # noqa: E721
+            type(self.model_executor) == HPUExecutor:  # noqa: E721
             self.model_executor.stop_profile()
         else:
             self.model_executor._run_workers("stop_profile")
diff --git a/vllm/engine/multiprocessing/engine.py b/vllm/engine/multiprocessing/engine.py
index 49500099fbcaf..3501f12c065cf 100644
--- a/vllm/engine/multiprocessing/engine.py
+++ b/vllm/engine/multiprocessing/engine.py
@@ -23,7 +23,7 @@
 # yapf: enable
 from vllm.envs import VLLM_RPC_TIMEOUT
 from vllm.executor.gpu_executor import GPUExecutor
-from vllm.executor.habana_executor import HabanaExecutor
+from vllm.executor.hpu_executor import HPUExecutor
 from vllm.logger import init_logger
 from vllm.outputs import RequestOutput
 from vllm.usage.usage_lib import UsageContext
@@ -366,14 +366,14 @@ def _alive(self):
 
     def start_profile(self) -> None:
         if type(self.engine.model_executor) is GPUExecutor or \
-                type(self.engine.model_executor) is HabanaExecutor:
+                type(self.engine.model_executor) is HPUExecutor:
             self.engine.model_executor.start_profile()
         else:
             self.engine.model_executor._run_workers("start_profile")
 
     def stop_profile(self) -> None:
         if type(self.engine.model_executor) is GPUExecutor or \
-                type(self.engine.model_executor) is HabanaExecutor:
+                type(self.engine.model_executor) is HPUExecutor:
             self.engine.model_executor.stop_profile()
         else:
             self.engine.model_executor._run_workers("stop_profile")
diff --git a/vllm/executor/habana_executor.py b/vllm/executor/hpu_executor.py
similarity index 97%
rename from vllm/executor/habana_executor.py
rename to vllm/executor/hpu_executor.py
index e6d0fbc0d431d..cc5609ebe5c8e 100644
--- a/vllm/executor/habana_executor.py
+++ b/vllm/executor/hpu_executor.py
@@ -21,7 +21,7 @@
 logger = init_logger(__name__)
 
 
-class HabanaExecutor(ExecutorBase):
+class HPUExecutor(ExecutorBase):
 
     uses_ray: bool = False
 
@@ -57,8 +57,8 @@ def _create_worker(self,
                        rank: int = 0,
                        distributed_init_method: Optional[str] = None):
         wrapper = WorkerWrapperBase(
-            worker_module_name="vllm.worker.habana_worker",
-            worker_class_name="HabanaWorker",
+            worker_module_name="vllm.worker.hpu_worker",
+            worker_class_name="HPUWorker",
         )
         wrapper.init_worker(**self._get_worker_kwargs(local_rank, rank,
                                                       distributed_init_method))
@@ -202,7 +202,7 @@ def shutdown(self) -> None:
         self.driver_worker.shutdown_inc()
 
 
-class HabanaExecutorAsync(HabanaExecutor, ExecutorAsyncBase):
+class HPUExecutorAsync(HPUExecutor, ExecutorAsyncBase):
 
     async def execute_model_async(
         self,
diff --git a/vllm/executor/ray_habana_executor.py b/vllm/executor/ray_hpu_executor.py
similarity index 99%
rename from vllm/executor/ray_habana_executor.py
rename to vllm/executor/ray_hpu_executor.py
index 645bceb1af446..343fa43b0eda1 100644
--- a/vllm/executor/ray_habana_executor.py
+++ b/vllm/executor/ray_hpu_executor.py
@@ -29,7 +29,7 @@
 logger = init_logger(__name__)
 
 
-class RayHabanaExecutor(DistributedGPUExecutor):
+class RayHPUExecutor(DistributedGPUExecutor):
 
     uses_ray: bool = True
 
@@ -90,8 +90,8 @@ def _get_worker_module_and_class(
             raise NotImplementedError(
                 "Speculative decoding is not implemented for HPU")
         else:
-            worker_module_name = "vllm.worker.habana_worker"
-            worker_class_name = "HabanaWorker"
+            worker_module_name = "vllm.worker.hpu_worker"
+            worker_class_name = "HPUWorker"
         return (worker_module_name, worker_class_name, worker_class_fn)
 
     def _get_worker_wrapper_args(self) -> Dict[str, Any]:
@@ -479,7 +479,7 @@ def __del__(self):
         self.shutdown()
 
 
-class RayHabanaExecutorAsync(RayHabanaExecutor, DistributedGPUExecutorAsync):
+class RayHPUExecutorAsync(RayHPUExecutor, DistributedGPUExecutorAsync):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
diff --git a/vllm/worker/habana_model_runner.py b/vllm/worker/hpu_model_runner.py
similarity index 99%
rename from vllm/worker/habana_model_runner.py
rename to vllm/worker/hpu_model_runner.py
index 2d72be5690664..b1b62e6bde7f6 100644
--- a/vllm/worker/habana_model_runner.py
+++ b/vllm/worker/hpu_model_runner.py
@@ -489,7 +489,7 @@ def from_broadcasted_tensor_dict(
         return cls(**tensor_dict)
 
 
-class HabanaModelRunnerBase(ModelRunnerBase[TModelInputForHPU]):
+class HPUModelRunnerBase(ModelRunnerBase[TModelInputForHPU]):
     """
     Helper class for shared methods between GPU model runners.
     """
@@ -1730,8 +1730,7 @@ def unwrap_model(model):
         return modules
 
 
-class HabanaModelRunner(
-        HabanaModelRunnerBase[ModelInputForHPUWithSamplingMetadata]):
+class HPUModelRunner(HPUModelRunnerBase[ModelInputForHPUWithSamplingMetadata]):
     """
     GPU model runner with sampling step.
     """
@@ -1872,7 +1871,7 @@ def execute_model(
     ) -> Optional[Union[List[SamplerOutput], IntermediateTensors]]:
         if num_steps > 1:
             raise ValueError(
-                "num_steps > 1 is not supported in HabanaModelRunner")
+                "num_steps > 1 is not supported in HPUModelRunner")
 
         if self.lora_config:
             assert model_input.lora_requests is not None
diff --git a/vllm/worker/habana_worker.py b/vllm/worker/hpu_worker.py
similarity index 99%
rename from vllm/worker/habana_worker.py
rename to vllm/worker/hpu_worker.py
index 7fc1e48b8c960..59a5adf65ebc1 100644
--- a/vllm/worker/habana_worker.py
+++ b/vllm/worker/hpu_worker.py
@@ -25,14 +25,14 @@
 from vllm.sequence import ExecuteModelRequest
 from vllm.utils import hpu_backend_string, hpu_device_string, is_fake_hpu
 from vllm.worker.cache_engine import CacheEngine
-from vllm.worker.habana_model_runner import HabanaModelRunner
+from vllm.worker.hpu_model_runner import HPUModelRunner
 from vllm.worker.model_runner_base import ModelRunnerBase
 from vllm.worker.worker_base import LocalOrDistributedWorkerBase, WorkerInput
 
 logger = init_logger(__name__)
 
 
-class HabanaWorker(LocalOrDistributedWorkerBase):
+class HPUWorker(LocalOrDistributedWorkerBase):
     """A worker class that executes (a partition of) the model on a HPU.
 
     Each worker is associated with a single HPU. The worker is responsible for
@@ -79,7 +79,7 @@ def __init__(
             from vllm.utils import init_cached_hf_modules
             init_cached_hf_modules()
 
-        self.model_runner: HabanaModelRunner = HabanaModelRunner(
+        self.model_runner: HPUModelRunner = HPUModelRunner(
             model_config,
             parallel_config,
             scheduler_config,