Mac Studio M3 Ultra 256GB经济之选
256 GB Unified (+76)
B
This setup is broadly balanced for this model.98 tok/s 解码
~$6,999 MSRP
Can InternLM Chat 7B run on NVIDIA B200 180GB?
YES — Runs Great
B66Good
Estimated from fit model
InternLM Chat 7B needs ~31.3 GB VRAM. NVIDIA B200 180GB has 180.0 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) — 31.3 GB, 98.0 tok/s, Runs well
31.3 GB required180.0 GB available
17% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
8K
Memory
31.3 GB / 180.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom18.0 GB
See how fast it feels
See how fast it feelsInternLM Chat 7B on NVIDIA B200 180GB
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 | B | Runs well | 98.0 tok/s | 2873 ms | 8K |
| Reasoning | B | Runs well | 98.0 tok/s | 2335 ms | 8K |
| RAG | B | Runs well | 98.0 tok/s | 3592 ms | 8K |
Quantization options
How InternLM Chat 7B (7B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B58 |
Q3_K_S | 3 | 3.4 GB | Low | B58 |
NVFP4 | 4 | 3.9 GB | Medium | B58 |
Q4_K_M | 4 | 4.3 GB | Medium | B58 |
Q5_K_M | 5 | 5.0 GB | High | B58 |
Q6_K | 6 | 5.7 GB | High | B58 |
Q8_0 | 8 | 7.5 GB | Very High | B58 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B58 |
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升级选项
能流畅运行 InternLM Chat 7B 的硬件
Frequently asked questions
Can NVIDIA B200 180GB run InternLM Chat 7B?
Yes, NVIDIA B200 180GB can run InternLM Chat 7B with a B grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does InternLM Chat 7B need?
InternLM Chat 7B (7B parameters) requires approximately 31.3 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 NVIDIA B200 180GB?
On NVIDIA B200 180GB, InternLM Chat 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 B200 180GB run InternLM Chat 7B for coding?
For coding workloads, InternLM Chat 7B on NVIDIA B200 180GB receives a B grade with 98.0 tok/s and 8K context.
What context window can InternLM Chat 7B use on NVIDIA B200 180GB?
On NVIDIA B200 180GB, 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internlm-chat-7b-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: