Can Qwen 3.5 397B A17B run on NVIDIA B200 180GB?

YES — With Q2_K

S97Excellent

Estimated from fit model

Qwen 3.5 397B A17B needs ~176.6 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q2_K quantization, expect ~121 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Qwen 3.5 397B A17B at Q4_K_M needs 263.9 GB — too much for NVIDIA B200 180GB (180.0 GB). Runs at Q2_K (176.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 263.9 GB, exceeds 180.0 GB available

263.9 GB required180.0 GB available

147% VRAM needed

83.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

41.2 tok/s

TTFT

4701 ms

Safe context

Memory

263.9 GB / 180.0 GB

Offload

30%

Memory breakdown

Weights242.2 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.5 397B A17B on NVIDIA B200 180GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 41.2 tok/s decode · 4.7s TTFT (warm) · 103 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	41.6 tok/s	2541 ms	4K
Coding	F	Too heavy	41.2 tok/s	4701 ms	4K
Agentic Coding	F	Too heavy	40.5 tok/s	6961 ms	4K
Reasoning	F	Too heavy	41.2 tok/s	5556 ms	4K
RAG	F	Too heavy	40.5 tok/s	8701 ms	4K

Inference speed

Qwen 3.5 397B A17B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 397B A17B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~7 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	6.9	Too big
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	4.4	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	4.1	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	3.2	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	3.2	Too big
RTX 5090 32GB	32 GB	Q4_K_M	2.4	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	2.4	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.3	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.1	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 3.5 397B A17B (397B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	154.8 GB	Low	F0
Q3_K_S	3	194.5 GB	Low	F0
NVFP4	4	222.3 GB	Medium	F0
Q4_K_M	4	242.2 GB	Medium	F0
Q5_K_M	5	285.8 GB	High	F0
Q6_K	6	325.5 GB	High	F0
Q8_0	8	424.8 GB	Very High	F0
F16	16	813.8 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 397B A17B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "Qwen/Qwen3.5-397B-A17B-Instruct" \
  --hf-file "Qwen3.5-397B-A17B-Instruct-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien Qwen 3.5 397B A17B

AMD Instinct MI350X 288GBOpción económica

288 GB VRAM (+108)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.78.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$8,000 MSRP

AMD Instinct MI325X 256GBMejor relación calidad-precio

256 GB VRAM (+76)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.39.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$20,000 MSRP

Frequently asked questions

Can NVIDIA B200 180GB run Qwen 3.5 397B A17B?

Yes, NVIDIA B200 180GB can run Qwen 3.5 397B A17B at Q2_K quantization (Runs with offload). The recommended Q4_K_M requires 263.9 GB which exceeds available memory, but at Q2_K it needs only 176.6 GB. Expected decode speed: 120.7 tok/s.

How much VRAM does Qwen 3.5 397B A17B need?

Qwen 3.5 397B A17B (397B parameters) requires approximately 263.9 GB at Q4_K_M quantization. On NVIDIA B200 180GB, it fits at Q2_K using 176.6 GB.

What is the best quantization for Qwen 3.5 397B A17B?

The recommended quantization is Q4_K_M, but on NVIDIA B200 180GB the best fitting quantization is Q2_K, which uses 176.6 GB.

What speed will Qwen 3.5 397B A17B run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Qwen 3.5 397B A17B achieves approximately 120.7 tokens per second decode speed with a time-to-first-token of 1604ms using Q2_K quantization.

Can NVIDIA B200 180GB run Qwen 3.5 397B A17B for coding?

For coding workloads, Qwen 3.5 397B A17B on NVIDIA B200 180GB receives a F grade with 41.2 tok/s and 4K context.

What context window can Qwen 3.5 397B A17B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Qwen 3.5 397B A17B can safely use up to 35K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 397B A17B feels slow on NVIDIA B200 180GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA B200 180GB See all hardware for Qwen 3.5 397B A17B

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