RX 7600 XT 16GBコスパ重視
16 GB VRAM (+8)288 GB/s (+32)
C
Raises estimated decode speed by about 51%.34.2 tok/sデコード
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can openchat 3.6 8b 20240522 IMat run on RX 580 8GB?
YES — Tight Fit
C49Usable
Estimated from fit model
openchat 3.6 8b 20240522 IMat needs ~7.5 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 7.5 GB, 22.6 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
22.6 tok/s
TTFT
8583 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsopenchat 3.6 8b 20240522 IMat on RX 580 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: 22.6 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 22.6 tok/s | 4681 ms | 24K |
| Coding | C | Tight fit | 22.6 tok/s | 8583 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 14.6 tok/s | 19325 ms | 24K |
| Reasoning | C | Tight fit | 22.6 tok/s | 10143 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 14.6 tok/s | 24157 ms | 24K |
Quantization options
How openchat 3.6 8b 20240522 IMat (8B params) fits at each quantization level on RX 580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run openchat 3.6 8b 20240522 IMat on your machine.
Run
lms load hf-legraphista--openchat-3-6-8b-20240522-imat-gguf && lms server startアップグレードオプション
openchat 3.6 8b 20240522 IMatを快適に動かすハードウェア
RX 9060 XT 16GBコスパ最良
16 GB VRAM (+8)320 GB/s (+64)
C
Raises estimated decode speed by about 83%.41.3 tok/sデコード
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
〜$349 MSRP
RX 7700 XT 12GBAMDアップグレード
12 GB VRAM (+4)432 GB/s (+176)
C
Raises estimated decode speed by about 135%.53.1 tok/sデコード
Raises estimated decode speed by about 135%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
Frequently asked questions
Can RX 580 8GB run openchat 3.6 8b 20240522 IMat?
Yes, RX 580 8GB can run openchat 3.6 8b 20240522 IMat with a C grade (Tight fit). Expected decode speed: 22.6 tok/s.
How much VRAM does openchat 3.6 8b 20240522 IMat need?
openchat 3.6 8b 20240522 IMat (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.
What is the best quantization for openchat 3.6 8b 20240522 IMat?
The recommended quantization for openchat 3.6 8b 20240522 IMat is Q4_K_M, which balances quality and memory efficiency.
What speed will openchat 3.6 8b 20240522 IMat run at on RX 580 8GB?
On RX 580 8GB, openchat 3.6 8b 20240522 IMat achieves approximately 22.6 tokens per second decode speed with a time-to-first-token of 8583ms using Q4_K_M quantization.
Can RX 580 8GB run openchat 3.6 8b 20240522 IMat for coding?
For coding workloads, openchat 3.6 8b 20240522 IMat on RX 580 8GB receives a C grade with 22.6 tok/s and 24K context.
What context window can openchat 3.6 8b 20240522 IMat use on RX 580 8GB?
On RX 580 8GB, openchat 3.6 8b 20240522 IMat can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if openchat 3.6 8b 20240522 IMat feels slow on RX 580 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/hf-legraphista--openchat-3-6-8b-20240522-imat-gguf-on-rx-580-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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