Can InternLM 7B run on NVIDIA A16 64GB?

YES — Runs Great

B69Good

Estimated from fit model

InternLM 7B needs ~19.7 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~98 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) — 19.7 GB, 98.0 tok/s, Runs well

19.7 GB required64.0 GB available

31% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

Memory

19.7 GB / 64.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsInternLM 7B on NVIDIA A16 64GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	B	Runs well	98.0 tok/s	1078 ms	8K
Coding	B	Runs well	98.0 tok/s	1976 ms	8K
Agentic Coding	A	Runs well	98.0 tok/s	2873 ms	8K
Reasoning	B	Runs well	98.0 tok/s	2335 ms	8K
RAG	A	Runs well	98.0 tok/s	3592 ms	8K

Quantization options

How InternLM 7B (7B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B61
Q3_K_S	3	3.4 GB	Low	B61
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs InternLM 7B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)

This setup is broadly balanced for this model.87.8 tok/s decode

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+219)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run InternLM 7B?

Yes, NVIDIA A16 64GB can run InternLM 7B with a B grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does InternLM 7B need?

InternLM 7B (7B parameters) requires approximately 19.7 GB of memory with Q4_K_M quantization.

What is the best quantization for InternLM 7B?

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

What speed will InternLM 7B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, InternLM 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run InternLM 7B for coding?

For coding workloads, InternLM 7B on NVIDIA A16 64GB receives a B grade with 98.0 tok/s and 8K context.

What context window can InternLM 7B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, InternLM 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 NVIDIA A16 64GB See all hardware for InternLM 7B

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