Nous Hermes 2 SOLAR 10.7B needs ~6.9 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q2_K quantization, expect ~11 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
3.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.3 tok/s
TTFT
44857 ms
Safe context
4K
Memory
9.3 GB / 6.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.0 tok/s | 21118 ms | 4K |
| Coding | F | Too heavy | 4.3 tok/s | 44857 ms | 4K |
| Agentic Coding | F | Too heavy | 3.3 tok/s | 85194 ms | 4K |
| Reasoning | F | Too heavy | 4.3 tok/s | 53013 ms | 4K |
| RAG | F | Too heavy | 3.3 tok/s | 106492 ms | 4K |
How Nous Hermes 2 SOLAR 10.7B (10.699999809265137B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | F0 |
Q3_K_S | 3 | 5.2 GB | Low | F0 |
NVFP4 | 4 | 6.0 GB | Medium | F0 |
Q4_K_M | 4 | 6.5 GB | Medium | F0 |
Q5_K_M | 5 | 7.7 GB | High | F0 |
Q6_K | 6 | 8.8 GB | High | F0 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Copy-paste commands to run Nous Hermes 2 SOLAR 10.7B on your machine.
Run
lms load hf-thebloke--nous-hermes-2-solar-10-7b-gguf && lms server startOpciones de mejora
Intel Arc A580 8GBMejora IntelHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 370%.
~$179 MSRP
Intel Arc B570 10GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$219 MSRP
Intel Arc B580 12GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$249 MSRP
RTX 4080 Super 16GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$999 MSRP
Yes, Intel Arc Pro A40 6GB can run Nous Hermes 2 SOLAR 10.7B at Q2_K quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 9.3 GB which exceeds available memory, but at Q2_K it needs only 6.9 GB. Expected decode speed: 10.6 tok/s.
Nous Hermes 2 SOLAR 10.7B (10.699999809265137B parameters) requires approximately 9.3 GB at Q4_K_M quantization. On Intel Arc Pro A40 6GB, it fits at Q2_K using 6.9 GB.
The recommended quantization is Q4_K_M, but on Intel Arc Pro A40 6GB the best fitting quantization is Q2_K, which uses 6.9 GB.
On Intel Arc Pro A40 6GB, Nous Hermes 2 SOLAR 10.7B achieves approximately 10.6 tokens per second decode speed with a time-to-first-token of 18226ms using Q2_K quantization.
For coding workloads, Nous Hermes 2 SOLAR 10.7B on Intel Arc Pro A40 6GB receives a F grade with 4.3 tok/s and 4K context.
On Intel Arc Pro A40 6GB, Nous Hermes 2 SOLAR 10.7B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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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