Mac Studio M2 Ultra 128GBコスパ重視
128 GB Unified (+48)
B
This setup is broadly balanced for this model.98 tok/sデコード
〜$3,999 MSRP
Can InternLM 7B run on NVIDIA H100 PCIe 80GB?
YES — Runs Great
B68Good
Estimated from fit model
InternLM 7B needs ~21.3 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 21.3 GB, 98.0 tok/s, Runs well
21.3 GB required80.0 GB available
27% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
8K
Memory
21.3 GB / 80.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsInternLM 7B on NVIDIA H100 PCIe 80GB
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 H100 PCIe 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B60 |
Q3_K_S | 3 | 3.4 GB | Low | B60 |
NVFP4 | 4 | 3.9 GB | Medium | B60 |
Q4_K_M | 4 | 4.3 GB | Medium | B60 |
Q5_K_M | 5 | 5.0 GB | High | B60 |
Q6_K | 6 | 5.7 GB | High | B60 |
Q8_0 | 8 | 7.5 GB | Very High | B61 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B61 |
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アップグレードオプション
InternLM 7Bを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA H100 PCIe 80GB run InternLM 7B?
Yes, NVIDIA H100 PCIe 80GB 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 21.3 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 H100 PCIe 80GB?
On NVIDIA H100 PCIe 80GB, 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 H100 PCIe 80GB run InternLM 7B for coding?
For coding workloads, InternLM 7B on NVIDIA H100 PCIe 80GB receives a B grade with 98.0 tok/s and 8K context.
What context window can InternLM 7B use on NVIDIA H100 PCIe 80GB?
On NVIDIA H100 PCIe 80GB, 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.
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<iframe src="https://willitrunai.com/embed/internlm-7b-on-h100-pcie-80gb" 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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