Can Qwen3.5 27B run on Tesla P40 24GB?

YES — With Offload

C48Usable

Estimated from fit model

Qwen3.5 27B needs ~23.2 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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) — 23.2 GB, 12.4 tok/s, Runs with offload

23.2 GB required24.0 GB available

97% VRAM used

Fit status

Runs with offload

Decode

12.4 tok/s

TTFT

15620 ms

Safe context

20K

Memory

23.2 GB / 24.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen3.5 27B on Tesla P40 24GB
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: 12.4 tok/s decode · 15.6s TTFT (warm) · 31 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	12.4 tok/s	8520 ms	20K
Coding	C	Runs with offload	12.4 tok/s	15620 ms	20K
Agentic Coding	D	Very compromised	7.3 tok/s	38393 ms	20K
Reasoning	C	Runs with offload	12.4 tok/s	18460 ms	20K
RAG	D	Very compromised	7.3 tok/s	47992 ms	20K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C50
Q3_K_S	3	13.2 GB	Low	C50
NVFP4	4

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

Upgrade options

Hardware that runs Qwen3.5 27B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 488%.72.9 tok/s decode

Raises estimated decode speed by about 488%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 269%.45.7 tok/s decode

Raises estimated decode speed by about 269%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 126%.28 tok/s decode

Raises estimated decode speed by about 126%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Qwen3.5 27B?

Yes, Tesla P40 24GB can run Qwen3.5 27B with a C grade (Runs with offload). Expected decode speed: 12.4 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 23.2 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 Tesla P40 24GB?

On Tesla P40 24GB, Qwen3.5 27B achieves approximately 12.4 tokens per second decode speed with a time-to-first-token of 15620ms using Q4_K_M quantization.

Can Tesla P40 24GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on Tesla P40 24GB receives a C grade with 12.4 tok/s and 20K context.

What context window can Qwen3.5 27B use on Tesla P40 24GB?

On Tesla P40 24GB, Qwen3.5 27B can safely use up to 20K 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 27B feels slow on Tesla P40 24GB?

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 Tesla P40 24GB See all hardware for Qwen3.5 27B

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