Can Qwen3.5 27B run on NVIDIA A40 48GB?

YES — Runs Great

C50Usable

Estimated from fit model

Qwen3.5 27B needs ~25.6 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 25.6 GB, 33.0 tok/s, Runs well

25.6 GB required48.0 GB available

53% VRAM used

Fit status

Runs well

Decode

33.0 tok/s

TTFT

5873 ms

Safe context

129K

Memory

25.6 GB / 48.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen3.5 27B on NVIDIA A40 48GB
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: 33.0 tok/s decode · 5.9s TTFT (warm) · 82 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	33.0 tok/s	3204 ms	129K
Coding	C	Runs well	33.0 tok/s	5873 ms	129K
Agentic Coding	C	Runs well	33.0 tok/s	8543 ms	129K
Reasoning	C	Runs well	33.0 tok/s	6941 ms	129K
RAG	C	Runs well	33.0 tok/s	10679 ms	129K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C44
Q3_K_S	3	13.2 GB	Low	C44
NVFP4	4	15.1 GB	Medium	C45
Q4_K_M	4	16.5 GB	Medium	C45
Q5_K_M	5	19.4 GB	High	C46
Q6_K	6	22.1 GB	High	C47
Q8_0Best for your GPU	8	28.9 GB	Very High	C48
F16	16	55.4 GB	Maximum	F0

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

アップグレードオプション

Qwen3.5 27Bを快適に動かすハードウェア

AMD Instinct MI210 64GBコスパ重視

64 GB VRAM (+16)1638 GB/s (+942)

Raises estimated decode speed by about 105%.67.6 tok/sデコード

Raises estimated decode speed by about 105%.

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

〜$10,000 MSRP

Frequently asked questions

Can NVIDIA A40 48GB run Qwen3.5 27B?

Yes, NVIDIA A40 48GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 33.0 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 25.6 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 A40 48GB?

On NVIDIA A40 48GB, Qwen3.5 27B achieves approximately 33.0 tokens per second decode speed with a time-to-first-token of 5873ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on NVIDIA A40 48GB receives a C grade with 33.0 tok/s and 129K context.

What context window can Qwen3.5 27B use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, Qwen3.5 27B can safely use up to 129K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A40 48GB See all hardware for Qwen3.5 27B

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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-27b-gguf-on-a40-48gb" 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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