RTX 3060 12GBOpción económica12 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.55.6 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
Can stabilityai japanese stablelm base gamma 7b run on RTX 3060 Ti 8GB?
YES — Tight Fit
C53Usable
Estimated from fit model
stabilityai japanese stablelm base gamma 7b needs ~7.1 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~71 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: 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) — 7.1 GB, 71.3 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
71.3 tok/s
TTFT
2714 ms
Safe context
34K
Memory
7.1 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsstabilityai japanese stablelm base gamma 7b on RTX 3060 Ti 8GB
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: 71.3 tok/s decode · 2.7s TTFT (warm) · 178 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 | Tight fit | 71.3 tok/s | 1480 ms | 34K |
| Coding | C | Tight fit | 71.3 tok/s | 2714 ms | 34K |
| Agentic Coding | C | Runs with offload | 71.3 tok/s | 3947 ms | 34K |
| Reasoning | C | Tight fit | 71.3 tok/s | 3207 ms | 34K |
| RAG | C | Runs with offload | 71.3 tok/s | 4934 ms | 34K |
Quantization options
How stabilityai japanese stablelm base gamma 7b (7B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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 startOpciones de mejora
Hardware que ejecuta bien stabilityai japanese stablelm base gamma 7b
RTX 3060 12GBOpción económicaRTX 5070 12GBMejor relación calidad-precio12 GB VRAM (+4)672 GB/s (+224)
C
Sube la velocidad estimada de decodificación alrededor de un 37%.98 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 37%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$549 MSRP
RTX 4070 Super 12GBMejora NVIDIA12 GB VRAM (+4)504 GB/s (+56)
C
Sube la velocidad estimada de decodificación alrededor de un 27%.90.9 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 27%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$599 MSRP
Frequently asked questions
Can RTX 3060 Ti 8GB run stabilityai japanese stablelm base gamma 7b?
Yes, RTX 3060 Ti 8GB can run stabilityai japanese stablelm base gamma 7b with a C grade (Tight fit). Expected decode speed: 71.3 tok/s.
How much VRAM does stabilityai japanese stablelm base gamma 7b need?
stabilityai japanese stablelm base gamma 7b (7B parameters) requires approximately 7.1 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 RTX 3060 Ti 8GB?
On RTX 3060 Ti 8GB, stabilityai japanese stablelm base gamma 7b achieves approximately 71.3 tokens per second decode speed with a time-to-first-token of 2714ms using Q4_K_M quantization.
Can RTX 3060 Ti 8GB run stabilityai japanese stablelm base gamma 7b for coding?
For coding workloads, stabilityai japanese stablelm base gamma 7b on RTX 3060 Ti 8GB receives a C grade with 71.3 tok/s and 34K context.
What context window can stabilityai japanese stablelm base gamma 7b use on RTX 3060 Ti 8GB?
On RTX 3060 Ti 8GB, stabilityai japanese stablelm base gamma 7b can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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