B200 target: README.md

README.md

@@ -30,15 +30,15 @@ original task note (Chinese).
 
 ## 2. Hardware and software
 
-Tested target: 4x RTX 5090 (32 GB each). Per GPU one vLLM server and one client shard.
+Target: 4x NVIDIA B200 (180 GB each); also runs on 32 GB GPUs. Per GPU one vLLM server and one client shard.
 VRAM: Qwen3-VL-8B bf16 weights ~16.4 GB, Qwen3-VL-2B ~4.4 GB; the launcher uses
-`--gpu-memory-utilization 0.85`. If vLLM fails with out-of-memory at start-up, lower `MAX_SEQS`
+`--gpu-memory-utilization 0.85`. On 32 GB GPUs, if vLLM fails with out-of-memory at start-up, lower `MAX_SEQS`
 in `code/run_remote.sh` (e.g. 64 -> 32 for step 1); do not lower the resolution or the frame count.
 
 ```bash
 # Python 3.12 environment
 python3 -m venv pool-env && source pool-env/bin/activate
-pip install -r code/requirements.txt          # vllm 0.11.0 + torch 2.8 (CUDA 12.8 wheels; sm_120 for RTX 5090)
+pip install -r code/requirements.txt          # vllm 0.11.0 + torch 2.8 (CUDA 12.8 wheels; Blackwell B200 / RTX 5090)
 pip install -U "huggingface_hub[cli]"
 # models (pinned revisions; served under their hub names)
 huggingface-cli download Qwen/Qwen3-VL-8B-Instruct --revision 0c351dd01ed87e9c1b53cbc748cba10e6187ff3b
@@ -78,7 +78,7 @@ re-run at any time and continues where it stopped (rows are keyed by item_id).
 ```bash
 cd pool
 STEP=1 bash code/run_remote.sh --plan-only    # 299,366 items expected before the first run
-STEP=1 bash code/run_remote.sh                # ~1.5-2 h on 4x 5090 (estimate)
+STEP=1 bash code/run_remote.sh                # ~1-2 h on 4x B200 (estimate)
 STEP=2 bash code/run_remote.sh --plan-only    # survivors of step 1 that have frames (upper bound 183,196)
 STEP=2 bash code/run_remote.sh                # ~1.5-2.5 h (estimate)
 STEP=3 bash code/run_remote.sh --plan-only
@@ -90,7 +90,7 @@ threads per GPU; defaults 32 / 16 / 8 for steps 1 / 2 / 3), `PORT0`, `GPU_UTIL`,
 Logs: `logs/vllm_s<STEP>_gpu<i>.log`, `logs/client_s<STEP>_gpu<i>.log`.
 
 The time estimates come from throughput on the origin cluster's smaller GPU slices and were not
-measured on a 5090; step 1 calibrates them (its client log prints items/s).
+measured on this hardware; step 1 calibrates them (its client log prints items/s).
 
 ## 5. Progress and expected counts
 
@@ -115,8 +115,8 @@ adapted to the machine (ports, GPU count, paths) but not the vLLM model argument
 
 ## 7. Known limits
 
-- vLLM 0.11.0 needs the CUDA 12.8 torch 2.8 wheels for RTX 5090 (sm_120). If `pip install vllm==0.11.0`
-  pulls a torch without sm_120 support, install torch 2.8.0 from the cu128 index first.
+- vLLM 0.11.0 with the CUDA 12.8 torch 2.8 wheels (Blackwell B200 / RTX 5090). If `pip install vllm==0.11.0`
+  pulls a torch without Blackwell support, install torch 2.8.0 from the cu128 index first.
 - `--max-logprobs 20` and `continue_final_message` are vLLM features; the client will not work against
   other serving stacks.
 - The frame cache is a snapshot; missing directories for some `video_id` values are expected (see 5).