japanese stablelm instruct gamma 7B needs ~6.6 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~27 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
0.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.4 GB host RAM)
Decode
26.6 tok/s
TTFT
7272 ms
Safe context
4K
Memory
6.6 GB / 6.0 GB
Offload
10%
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 30.7 tok/s | 3444 ms | 4K |
| Coding | D | Very compromised | 26.6 tok/s | 7272 ms | 4K |
| Agentic Coding | F | Too heavy | 20.6 tok/s | 13685 ms | 4K |
| Reasoning | D | Very compromised | 26.6 tok/s | 8594 ms | 4K |
| RAG | F | Too heavy | 20.6 tok/s | 17107 ms | 4K |
How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C54 |
Q3_K_SBest for your GPU | 3 | 3.4 GB | Low | C53 |
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
RTX 3050 8GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
RTX 5060 8GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 141%.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 66%.
~$299 MSRP
Yes, RTX 2060 6GB can run japanese stablelm instruct gamma 7B with a D grade (Very compromised). Expected decode speed: 26.6 tok/s.
japanese stablelm instruct gamma 7B (7B parameters) requires approximately 6.6 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 RTX 2060 6GB, japanese stablelm instruct gamma 7B achieves approximately 26.6 tokens per second decode speed with a time-to-first-token of 7272ms using Q4_K_M quantization.
For coding workloads, japanese stablelm instruct gamma 7B on RTX 2060 6GB receives a D grade with 26.6 tok/s and 4K context.
On RTX 2060 6GB, japanese stablelm instruct gamma 7B can safely use up to 4K 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-rtx-2060-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
3.9 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 4.3 GB | Medium | F0 |
Q5_K_M | 5 | 5.0 GB | High | F0 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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.