Can Qwen3.5 35B A3B run on NVIDIA V100 32GB?

Q: What should I upgrade first if Qwen3.5 35B A3B feels slow on NVIDIA V100 32GB?

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

YES — Tight Fit

C50Usable

Estimated from fit model

Qwen3.5 35B A3B needs ~29.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 29.9 GB, 28.2 tok/s, Tight fit

29.9 GB required32.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

28.2 tok/s

TTFT

6855 ms

Safe context

24K

Memory

29.9 GB / 32.0 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen3.5 35B A3B on NVIDIA V100 32GB
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: 28.2 tok/s decode · 6.9s TTFT (warm) · 71 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	C	Tight fit	28.2 tok/s	3739 ms	24K
Coding	C	Tight fit	28.2 tok/s	6855 ms	24K
Agentic Coding	D	Runs with offload (needs ~1.2 GB host RAM)	21.8 tok/s	12890 ms	24K
Reasoning	C	Tight fit	28.2 tok/s	8101 ms	24K
RAG	D	Runs with offload (needs ~1.2 GB host RAM)	21.8 tok/s	16112 ms

Quantization options

How Qwen3.5 35B A3B (35B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	C49
Q3_K_S	3	17.2 GB	Low	C50
NVFP4	4

Get started

Copy-paste commands to run Qwen3.5 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-35B-A3B-GGUF" \
  --hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen3.5 35B A3B well

RTX A6000 48GBBudget pick

48 GB VRAM (+16)

Adds memory headroom for longer context windows and future model growth.27.3 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+444)

Raises estimated decode speed by about 88%.52.9 tok/s decode

Raises estimated decode speed by about 88%.

Adds memory headroom for longer context windows and future model growth.

~$4,999 MSRP

NVIDIA L40 48GBNVIDIA upgrade

48 GB VRAM (+16)

Adds memory headroom for longer context windows and future model growth.31.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$5,500 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run Qwen3.5 35B A3B?

Yes, NVIDIA V100 32GB can run Qwen3.5 35B A3B with a C grade (Tight fit). Expected decode speed: 28.2 tok/s.

How much VRAM does Qwen3.5 35B A3B need?

Qwen3.5 35B A3B (35B parameters) requires approximately 29.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 35B A3B?

The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 35B A3B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen3.5 35B A3B achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6855ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Qwen3.5 35B A3B for coding?

For coding workloads, Qwen3.5 35B A3B on NVIDIA V100 32GB receives a C grade with 28.2 tok/s and 24K context.

What context window can Qwen3.5 35B A3B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen3.5 35B A3B can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3.5 35B A3B feels slow on NVIDIA V100 32GB?

See all results for NVIDIA V100 32GB See all hardware for Qwen3.5 35B A3B

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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-35b-a3b-gguf-on-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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