Can Qwen3.5 27B run on NVIDIA B200 180GB?

YES — Runs Great

C47Usable

Estimated from fit model

Qwen3.5 27B needs ~38.8 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~378 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 38.8 GB, 378.0 tok/s, Runs well

38.8 GB required180.0 GB available

22% VRAM used

Fit status

Runs well

Decode

378.0 tok/s

TTFT

512 ms

Safe context

730K

Memory

38.8 GB / 180.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsQwen3.5 27B on NVIDIA B200 180GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 378.0 tok/s decode · 512ms TTFT (warm) · 945 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	378.0 tok/s	350 ms	730K
Coding	C	Runs well	378.0 tok/s	512 ms	730K
Agentic Coding	C	Runs well	378.0 tok/s	745 ms	730K
Reasoning	C	Runs well	378.0 tok/s	605 ms	730K
RAG	C	Runs well	378.0 tok/s	931 ms	730K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	D38
Q3_K_S	3	13.2 GB	Low	D38
NVFP4	4	15.1 GB	Medium	D38
Q4_K_M	4	16.5 GB	Medium	D38
Q5_K_M	5	19.4 GB	High	D38
Q6_K	6	22.1 GB	High	D38
Q8_0	8	28.9 GB	Very High	D39
F16Best for your GPU	16	55.4 GB	Maximum	C42

Get started

Copy-paste commands to run Qwen3.5 27B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-27B-GGUF" \
  --hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can NVIDIA B200 180GB run Qwen3.5 27B?

Yes, NVIDIA B200 180GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 378.0 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 38.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 27B?

The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 27B run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Qwen3.5 27B achieves approximately 378.0 tokens per second decode speed with a time-to-first-token of 512ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on NVIDIA B200 180GB receives a C grade with 378.0 tok/s and 730K context.

What context window can Qwen3.5 27B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Qwen3.5 27B can safely use up to 730K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for Qwen3.5 27B

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