Can InternLM Chat 7B run on Tesla P40 24GB?

YES — Runs Great

A74Great

Estimated from fit model

InternLM Chat 7B needs ~15.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 15.7 GB, 47.8 tok/s, Runs well

15.7 GB required24.0 GB available

65% VRAM used

Fit status

Runs well

Decode

47.8 tok/s

TTFT

4050 ms

Safe context

Memory

15.7 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsInternLM Chat 7B on Tesla P40 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 47.8 tok/s decode · 4.0s TTFT (warm) · 120 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	47.8 tok/s	2209 ms	8K
Coding	A	Runs well	47.8 tok/s	4050 ms	8K
Agentic Coding	A	Runs with offload	47.8 tok/s	5890 ms	8K
Reasoning	A	Runs well	47.8 tok/s	4786 ms	8K
RAG	A	Runs with offload	47.8 tok/s	7363 ms	8K

Inference speed

InternLM Chat 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for InternLM Chat 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quantization options

How InternLM Chat 7B (7B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B65
Q3_K_S	3	3.4 GB	Low	B65
NVFP4	4

Get started

Copy-paste commands to run InternLM Chat 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "InternLM/InternLM-Chat-7B" \
  --hf-file "InternLM-Chat-7B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your Tesla P40 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s
Qwen 3.5 27B	27B	S	13.4 tok/s

Frequently asked questions

Can Tesla P40 24GB run InternLM Chat 7B?

Yes, Tesla P40 24GB can run InternLM Chat 7B with a A grade (Runs well). Expected decode speed: 47.8 tok/s.

How much VRAM does InternLM Chat 7B need?

InternLM Chat 7B (7B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.

What is the best quantization for InternLM Chat 7B?

The recommended quantization for InternLM Chat 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will InternLM Chat 7B run at on Tesla P40 24GB?

On Tesla P40 24GB, InternLM Chat 7B achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4050ms using Q4_K_M quantization.

Can Tesla P40 24GB run InternLM Chat 7B for coding?

For coding workloads, InternLM Chat 7B on Tesla P40 24GB receives a A grade with 47.8 tok/s and 8K context.

What context window can InternLM Chat 7B use on Tesla P40 24GB?

On Tesla P40 24GB, InternLM Chat 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for InternLM Chat 7B

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