internlm2 math plus 7b IMat needs ~8.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~58 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
57.7 tok/s
TTFT
3357 ms
Safe context
320K
Memory
8.4 GB / 24.0 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 | 57.7 tok/s | 1831 ms | 320K |
| Coding | C | Runs well | 57.7 tok/s | 3357 ms | 320K |
| Agentic Coding | C | Runs well | 57.7 tok/s | 4883 ms | 320K |
| Reasoning | C | Runs well | 57.7 tok/s | 3968 ms | 320K |
| RAG | C | Runs well | 57.7 tok/s | 6104 ms | 320K |
How internlm2 math plus 7b IMat (7B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 | 3.9 GB | Medium | C44 |
Q4_K_M | 4 | 4.3 GB | Medium | C45 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C45 |
Q8_0 | 8 | 7.5 GB | Very High | C46 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C50 |
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
Raises estimated decode speed by about 53%.
Adds memory headroom for longer context windows and future model growth.
〜$1,899 MSRP
RTX 5090 32GBコスパ最良Raises estimated decode speed by about 131%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
〜$1,999 MSRP
Yes, Intel Arc Pro B60 24GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 57.7 tok/s.
internlm2 math plus 7b IMat (7B parameters) requires approximately 8.4 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 Pro B60 24GB, internlm2 math plus 7b IMat achieves approximately 57.7 tokens per second decode speed with a time-to-first-token of 3357ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 7b IMat on Intel Arc Pro B60 24GB receives a C grade with 57.7 tok/s and 320K context.
On Intel Arc Pro B60 24GB, internlm2 math plus 7b IMat can safely use up to 320K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
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