RX 7700 XT 12GB经济之选
12 GB VRAM (+4)432 GB/s (+144)
C
Raises estimated decode speed by about 53%.53.1 tok/s 解码
Raises estimated decode speed by about 53%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can aya expanse 8b run on Radeon PRO W7600 8GB?
YES — Tight Fit
C51Usable
Estimated from fit model
aya expanse 8b needs ~7.5 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~35 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, 34.8 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
34.8 tok/s
TTFT
5560 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 feelsaya expanse 8b on Radeon PRO W7600 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: 34.8 tok/s decode · 5.6s TTFT (warm) · 87 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 | 34.8 tok/s | 3033 ms | 24K |
| Coding | C | Tight fit | 34.8 tok/s | 5560 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 23.2 tok/s | 12115 ms | 24K |
| Reasoning | C | Tight fit | 34.8 tok/s | 6571 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 23.2 tok/s | 15144 ms | 24K |
Inference speed
aya expanse 8b inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for aya expanse 8b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 95.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 90.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 77.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 45.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 40.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 39.6 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How aya expanse 8b (8B params) fits at each quantization level on Radeon PRO W7600 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 aya expanse 8b on your machine.
Run
lms load hf-bartowski--aya-expanse-8b-gguf && lms server start升级选项
能流畅运行 aya expanse 8b 的硬件
RX 6700 XT 12GB最佳性价比
12 GB VRAM (+4)384 GB/s (+96)
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 (+352)
C
Raises estimated decode speed by about 134%.81.3 tok/s 解码
Raises estimated decode speed by about 134%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Frequently asked questions
Can Radeon PRO W7600 8GB run aya expanse 8b?
Yes, Radeon PRO W7600 8GB can run aya expanse 8b with a C grade (Tight fit). Expected decode speed: 34.8 tok/s.
How much VRAM does aya expanse 8b need?
aya expanse 8b (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.
What is the best quantization for aya expanse 8b?
The recommended quantization for aya expanse 8b is Q4_K_M, which balances quality and memory efficiency.
What speed will aya expanse 8b run at on Radeon PRO W7600 8GB?
On Radeon PRO W7600 8GB, aya expanse 8b achieves approximately 34.8 tokens per second decode speed with a time-to-first-token of 5560ms using Q4_K_M quantization.
Can Radeon PRO W7600 8GB run aya expanse 8b for coding?
For coding workloads, aya expanse 8b on Radeon PRO W7600 8GB receives a C grade with 34.8 tok/s and 24K context.
What context window can aya expanse 8b use on Radeon PRO W7600 8GB?
On Radeon PRO W7600 8GB, aya expanse 8b 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 aya expanse 8b feels slow on Radeon PRO W7600 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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