Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+76)
C
This setup is broadly balanced for this model.98 tok/sデコード
〜$6,999 MSRP
Can internlm2 math plus 7b IMat run on NVIDIA B200 180GB?
YES — Runs Great
C44Usable
Estimated from fit model
internlm2 math plus 7b IMat needs ~24.3 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) — 24.3 GB, 98.0 tok/s, Runs well
24.3 GB required180.0 GB available
14% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
3.1M
Memory
24.3 GB / 180.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom18.0 GB
See how fast it feels
See how fast it feelsinternlm2 math plus 7b IMat on NVIDIA B200 180GB
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: 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 | C | Runs well | 98.0 tok/s | 1078 ms | 3.1M |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 3.1M |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 3.1M |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 3.1M |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 3.1M |
Quantization options
How internlm2 math plus 7b IMat (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 | D37 |
Q3_K_S | 3 | 3.4 GB | Low | D37 |
NVFP4 | 4 | 3.9 GB | Medium | D37 |
Q4_K_M | 4 | 4.3 GB | Medium | D37 |
Q5_K_M | 5 | 5.0 GB | High | D37 |
Q6_K | 6 | 5.7 GB | High | D37 |
Q8_0 | 8 | 7.5 GB | Very High | D37 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | D37 |
Get started
Copy-paste commands to run internlm2 math plus 7b IMat on your machine.
Run
lms load hf-legraphista--internlm2-math-plus-7b-imat-gguf && lms server startアップグレードオプション
internlm2 math plus 7b IMatを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA B200 180GB run internlm2 math plus 7b IMat?
Yes, NVIDIA B200 180GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does internlm2 math plus 7b IMat need?
internlm2 math plus 7b IMat (7B parameters) requires approximately 24.3 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 math plus 7b IMat?
The recommended quantization for internlm2 math plus 7b IMat is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 math plus 7b IMat run at on NVIDIA B200 180GB?
On NVIDIA B200 180GB, internlm2 math plus 7b IMat 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 internlm2 math plus 7b IMat for coding?
For coding workloads, internlm2 math plus 7b IMat on NVIDIA B200 180GB receives a C grade with 98.0 tok/s and 3.1M context.
What context window can internlm2 math plus 7b IMat use on NVIDIA B200 180GB?
On NVIDIA B200 180GB, internlm2 math plus 7b IMat can safely use up to 3.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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