internlm2 math plus 7b IMat needs ~7.0 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~48 tok/s.
Operating mode
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
Fit status
Runs well
Decode
48.1 tok/s
TTFT
4029 ms
Safe context
75K
Memory
7.0 GB / 10.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 48.1 tok/s | 2197 ms | 75K |
| Coding | C | Runs well | 48.1 tok/s | 4029 ms | 75K |
| Agentic Coding | C | Runs well | 48.1 tok/s | 5860 ms | 75K |
| Reasoning | C | Runs well | 48.1 tok/s | 4761 ms | 75K |
| RAG | C | Runs well | 48.1 tok/s | 7325 ms | 75K |
How internlm2 math plus 7b IMat (7B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C50 |
Q3_K_S | 3 | 3.4 GB | Low | C51 |
NVFP4 | 4 |
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 startUpgrade options
Yes, Intel Arc B570 10GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 48.1 tok/s.
internlm2 math plus 7b IMat (7B parameters) requires approximately 7.0 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 math plus 7b IMat is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc B570 10GB, internlm2 math plus 7b IMat achieves approximately 48.1 tokens per second decode speed with a time-to-first-token of 4029ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 7b IMat on Intel Arc B570 10GB receives a C grade with 48.1 tok/s and 75K context.
On Intel Arc B570 10GB, internlm2 math plus 7b IMat can safely use up to 75K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-legraphista--internlm2-math-plus-7b-imat-gguf-on-arc-b570-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C52 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_KBest for your GPU | 6 | 5.7 GB | High | C52 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
