RX 7700 XT 12GBBudget pick
12 GB VRAM (+4)432 GB/s (+152)
C
Raises estimated decode speed by about 82%.60.7 tok/s decode
Raises estimated decode speed by about 82%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can japanese stablelm instruct gamma 7B run on RX 6650 XT 8GB?
YES — Tight Fit
C50Usable
○Estimated from fit model
japanese stablelm instruct gamma 7B needs ~6.8 GB VRAM. RX 6650 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~33 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Share:
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) — 6.8 GB, 33.4 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
33.4 tok/s
TTFT
5789 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsjapanese stablelm instruct gamma 7B on RX 6650 XT 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: 33.4 tok/s decode · 5.8s TTFT (warm) · 84 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 | 33.4 tok/s | 3158 ms | 40K |
| Coding | C | Tight fit | 33.4 tok/s | 5789 ms | 40K |
| Agentic Coding | C | Runs with offload | 33.4 tok/s | 8420 ms | 40K |
| Reasoning | C | Tight fit | 33.4 tok/s | 6842 ms | 40K |
| RAG | C | Runs with offload | 33.4 tok/s | 10525 ms | 40K |
Quantization options
How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on RX 6650 XT 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 |
Get started
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
Hardware that runs japanese stablelm instruct gamma 7B well
RX 6700 XT 12GBBest value
12 GB VRAM (+4)384 GB/s (+104)
C
Raises estimated decode speed by about 40%.46.8 tok/s decode
Raises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
RX 9070 16GBAMD upgrade
16 GB VRAM (+8)640 GB/s (+360)
C
Raises estimated decode speed by about 178%.92.9 tok/s decode
Raises estimated decode speed by about 178%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP