MacBook Pro M4 Max 48GBOpção econômica
48 GB Unified (+16)
C
This setup is broadly balanced for this model.87.8 tok/s decodificação
~$2,499 MSRP
Can stabilityai japanese stablelm base gamma 7b run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
stabilityai japanese stablelm base gamma 7b needs ~9.2 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~88 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 9.2 GB, 88.4 tok/s, Runs well
9.2 GB required32.0 GB available
29% VRAM used
Fit status
Runs well
Decode
88.4 tok/s
TTFT
2189 ms
Safe context
461K
Memory
9.2 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsstabilityai japanese stablelm base gamma 7b on Radeon AI PRO R9700 32GB
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: 88.4 tok/s decode · 2.2s TTFT (warm) · 221 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 | 88.4 tok/s | 1194 ms | 461K |
| Coding | C | Runs well | 88.4 tok/s | 2189 ms | 461K |
| Agentic Coding | C | Runs well | 88.4 tok/s | 3184 ms | 461K |
| Reasoning | C | Runs well | 88.4 tok/s | 2587 ms | 461K |
| RAG | C | Runs well | 88.4 tok/s | 3981 ms | 461K |
Quantization options
How stabilityai japanese stablelm base gamma 7b (7B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C43 |
Q3_K_S | 3 | 3.4 GB | Low | C43 |
NVFP4 | 4 | 3.9 GB | Medium | C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C43 |
Q5_K_M | 5 | 5.0 GB | High | C43 |
Q6_K | 6 | 5.7 GB | High | C44 |
Q8_0 | 8 | 7.5 GB | Very High | C44 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C47 |
Get started
Copy-paste commands to run stabilityai japanese stablelm base gamma 7b on your machine.
Run
lms load hf-richarderkhov--stabilityai---japanese-stablelm-base-gamma-7b-gguf && lms server startOpções de upgrade
Hardware que roda bem stabilityai japanese stablelm base gamma 7b
Frequently asked questions
Can Radeon AI PRO R9700 32GB run stabilityai japanese stablelm base gamma 7b?
Yes, Radeon AI PRO R9700 32GB can run stabilityai japanese stablelm base gamma 7b with a C grade (Runs well). Expected decode speed: 88.4 tok/s.
How much VRAM does stabilityai japanese stablelm base gamma 7b need?
stabilityai japanese stablelm base gamma 7b (7B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
What is the best quantization for stabilityai japanese stablelm base gamma 7b?
The recommended quantization for stabilityai japanese stablelm base gamma 7b is Q4_K_M, which balances quality and memory efficiency.
What speed will stabilityai japanese stablelm base gamma 7b run at on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, stabilityai japanese stablelm base gamma 7b achieves approximately 88.4 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run stabilityai japanese stablelm base gamma 7b for coding?
For coding workloads, stabilityai japanese stablelm base gamma 7b on Radeon AI PRO R9700 32GB receives a C grade with 88.4 tok/s and 461K context.
What context window can stabilityai japanese stablelm base gamma 7b use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, stabilityai japanese stablelm base gamma 7b can safely use up to 461K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-richarderkhov--stabilityai---japanese-stablelm-base-gamma-7b-gguf-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: