RX 7700 XT 12GB经济之选
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.53.1 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can openchat 3.6 8b 20240522 IMat run on RX 5700 XT 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
openchat 3.6 8b 20240522 IMat needs ~7.5 GB VRAM. RX 5700 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 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, 47.7 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
47.7 tok/s
TTFT
4055 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 5700 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: 47.7 tok/s decode · 4.1s TTFT (warm) · 119 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.
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 | 47.7 tok/s | 2212 ms | 24K |
| Coding | C | Tight fit | 47.7 tok/s | 4055 ms | 24K |
| Agentic Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 31.9 tok/s | 8836 ms | 24K |
| Reasoning | C | Tight fit | 47.7 tok/s | 4793 ms | 24K |
| RAG | C | Runs with offload (needs ~0.3 GB host RAM) | 31.9 tok/s | 11046 ms | 24K |
Quantization options
How openchat 3.6 8b 20240522 IMat (8B params) fits at each quantization level on RX 5700 XT 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升级选项
能流畅运行 openchat 3.6 8b 20240522 IMat 的硬件
RX 6700 XT 12GB最佳性价比
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.40.9 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
RX 9070 16GBAMD 升级
16 GB VRAM (+8)640 GB/s (+192)
C
Raises estimated decode speed by about 70%.81.3 tok/s 解码
Raises estimated decode speed by about 70%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Frequently asked questions
Can RX 5700 XT 8GB run openchat 3.6 8b 20240522 IMat?
Yes, RX 5700 XT 8GB can run openchat 3.6 8b 20240522 IMat with a C grade (Tight fit). Expected decode speed: 47.7 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 5700 XT 8GB?
On RX 5700 XT 8GB, openchat 3.6 8b 20240522 IMat achieves approximately 47.7 tokens per second decode speed with a time-to-first-token of 4055ms using Q4_K_M quantization.
Can RX 5700 XT 8GB run openchat 3.6 8b 20240522 IMat for coding?
For coding workloads, openchat 3.6 8b 20240522 IMat on RX 5700 XT 8GB receives a C grade with 47.7 tok/s and 24K context.
What context window can openchat 3.6 8b 20240522 IMat use on RX 5700 XT 8GB?
On RX 5700 XT 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 5700 XT 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-5700-xt-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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