Can Aya Expanse 32B run on Radeon RX 6850M XT 12GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Aya Expanse 32B needs ~24.1 GB but Radeon RX 6850M XT 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.1 GB, exceeds 12.0 GB available

24.1 GB required12.0 GB available

201% VRAM needed

12.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.3 tok/s

TTFT

82838 ms

Safe context

Memory

24.1 GB / 12.0 GB

Offload

50%

Memory breakdown

Weights19.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsAya Expanse 32B on Radeon RX 6850M XT 12GB
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: 2.3 tok/s decode · 82.8s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 24.1 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.6 tok/s	40494 ms	4K
Coding	F	Too heavy	2.3 tok/s	82838 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	139290 ms	4K
Reasoning	F	Too heavy	2.3 tok/s	97899 ms	4K
RAG	F	Too heavy	2.0 tok/s	174113 ms	4K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on Radeon RX 6850M XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	F0
Q3_K_S	3	15.7 GB	Low	F0
NVFP4	4	17.9 GB	Medium	F0
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

升级选项

能流畅运行 Aya Expanse 32B 的硬件

RX 7900 XTX 24GB经济之选

24 GB VRAM (+12)960 GB/s (+528)

Makes the model fit on the accelerator instead of staying completely out of reach.25.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GB最佳性价比

32 GB VRAM (+20)640 GB/s (+208)

Makes the model fit on the accelerator instead of staying completely out of reach.21 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Radeon Pro W6800 32GBAMD 升级

32 GB VRAM (+20)512 GB/s (+80)

Makes the model fit on the accelerator instead of staying completely out of reach.16 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,249 MSRP

Frequently asked questions

Can Radeon RX 6850M XT 12GB run Aya Expanse 32B?

No, Aya Expanse 32B requires more memory than Radeon RX 6850M XT 12GB provides.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 24.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Aya Expanse 32B?

The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Aya Expanse 32B run at on Radeon RX 6850M XT 12GB?

On Radeon RX 6850M XT 12GB, Aya Expanse 32B achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 82838ms using Q4_K_M quantization.

Can Radeon RX 6850M XT 12GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on Radeon RX 6850M XT 12GB receives a F grade with 2.3 tok/s and 4K context.

What context window can Aya Expanse 32B use on Radeon RX 6850M XT 12GB?

On Radeon RX 6850M XT 12GB, Aya Expanse 32B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Aya Expanse 32B feels slow on Radeon RX 6850M XT 12GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for Radeon RX 6850M XT 12GB See all hardware for Aya Expanse 32B

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