RX 9070 16GBBudget pick
16 GB VRAM (+4)640 GB/s (+304)
C
Raises estimated decode speed by about 141%.92.9 tok/s decode
Raises estimated decode speed by about 141%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
japanese stablelm instruct gamma 7B needs ~7.2 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~39 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
38.6 tok/s
TTFT
5021 ms
Safe context
110K
Memory
7.2 GB / 12.0 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 | 38.6 tok/s | 2739 ms | 110K |
| Coding | C | Runs well | 38.6 tok/s | 5021 ms | 110K |
| Agentic Coding | C | Runs well | 38.6 tok/s | 7303 ms | 110K |
| Reasoning | C | Runs well | 38.6 tok/s | 5934 ms | 110K |
| RAG | C | Runs well | 38.6 tok/s | 9129 ms | 110K |
How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run japanese stablelm instruct gamma 7B on your machine.
Run
lms load hf-thebloke--japanese-stablelm-instruct-gamma-7b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 141%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Raises estimated decode speed by about 135%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, Intel Arc A730M 12GB can run japanese stablelm instruct gamma 7B with a C grade (Runs well). Expected decode speed: 38.6 tok/s.
japanese stablelm instruct gamma 7B (7B parameters) requires approximately 7.2 GB of memory with Q4_K_M quantization.
The recommended quantization for japanese stablelm instruct gamma 7B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A730M 12GB, japanese stablelm instruct gamma 7B achieves approximately 38.6 tokens per second decode speed with a time-to-first-token of 5021ms using Q4_K_M quantization.
For coding workloads, japanese stablelm instruct gamma 7B on Intel Arc A730M 12GB receives a C grade with 38.6 tok/s and 110K context.
On Intel Arc A730M 12GB, japanese stablelm instruct gamma 7B can safely use up to 110K 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-thebloke--japanese-stablelm-instruct-gamma-7b-gguf-on-arc-a730m-12gb" 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 |
| C50 |
Q4_K_M | 4 | 4.3 GB | Medium | C51 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
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.