RTX 3080 10GBコスパ重視10 GB VRAM (+2)760 GB/s (+440)
B
Raises estimated decode speed by about 63%.84 tok/sデコード
Raises estimated decode speed by about 63%.
Adds memory headroom for longer context windows and future model growth.
〜$699 MSRP
Can stablelm 2 1 6b chat imatrix run on GTX 1080 8GB?
YES — Runs Great
C55Usable
Estimated from fit model
stablelm 2 1 6b chat imatrix needs ~6.4 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: 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) — 6.4 GB, 51.6 tok/s, Runs well
6.4 GB required8.0 GB available
80% VRAM used
Fit status
Runs well
Decode
51.6 tok/s
TTFT
3753 ms
Safe context
53K
Memory
6.4 GB / 8.0 GB
Memory breakdown
Weights3.7 GB
KV Cache0.7 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsstablelm 2 1 6b chat imatrix on GTX 1080 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: 51.6 tok/s decode · 3.8s TTFT (warm) · 129 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 51.6 tok/s | 2047 ms | 53K |
| Coding | C | Runs well | 51.6 tok/s | 3753 ms | 53K |
| Agentic Coding | C | Tight fit | 51.6 tok/s | 5459 ms | 53K |
| Reasoning | C | Runs well | 51.6 tok/s | 4435 ms | 53K |
| RAG | C | Tight fit | 51.6 tok/s | 6824 ms | 53K |
Quantization options
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C52 |
Q3_K_S | 3 | 2.9 GB | Low | C53 |
NVFP4 | 4 | 3.4 GB | Medium | C53 |
Q4_K_M | 4 | 3.7 GB | Medium | C53 |
Q5_K_M | 5 | 4.3 GB | High | C53 |
Q6_KBest for your GPU | 6 | 4.9 GB | High | C53 |
Q8_0 | 8 | 6.4 GB | Very High | F0 |
F16 | 16 | 12.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run stablelm 2 1 6b chat imatrix on your machine.
Run
lms load hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf && lms server startアップグレードオプション
stablelm 2 1 6b chat imatrixを快適に動かすハードウェア
Frequently asked questions
Can GTX 1080 8GB run stablelm 2 1 6b chat imatrix?
Yes, GTX 1080 8GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 51.6 tok/s.
How much VRAM does stablelm 2 1 6b chat imatrix need?
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 6.4 GB of memory with Q4_K_M quantization.
What is the best quantization for stablelm 2 1 6b chat imatrix?
The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.
What speed will stablelm 2 1 6b chat imatrix run at on GTX 1080 8GB?
On GTX 1080 8GB, stablelm 2 1 6b chat imatrix achieves approximately 51.6 tokens per second decode speed with a time-to-first-token of 3753ms using Q4_K_M quantization.
Can GTX 1080 8GB run stablelm 2 1 6b chat imatrix for coding?
For coding workloads, stablelm 2 1 6b chat imatrix on GTX 1080 8GB receives a C grade with 51.6 tok/s and 53K context.
What context window can stablelm 2 1 6b chat imatrix use on GTX 1080 8GB?
On GTX 1080 8GB, stablelm 2 1 6b chat imatrix can safely use up to 53K 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-crataco--stablelm-2-1-6b-chat-imatrix-gguf-on-gtx-1080-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: