Can Qwen3.5 122B A10B run on RX 590 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen3.5 122B A10B needs ~75.8 GB but RX 590 8GB only has 8.0 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

F16 (Maximum quality) — 266.1 GB, exceeds 8.0 GB available

266.1 GB required8.0 GB available

3326% VRAM needed

258.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

266.1 GB / 8.0 GB

Offload

100%

Memory breakdown

Weights250.1 GB

KV Cache14.3 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3.5 122B A10B on RX 590 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 75.8 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

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	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Qwen3.5 122B A10B (122B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	F0
Q3_K_S	3	59.8 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Qwen3.5 122B A10B well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+280)8000 GB/s (+7744)

Makes the model fit on the accelerator instead of staying completely out of reach.90.9 tok/s decode

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.

~$8,000 MSRP

AMD Instinct MI300A 128GBBest value

128 GB VRAM (+120)5300 GB/s (+5044)

Makes the model fit on the accelerator instead of staying completely out of reach.57.7 tok/s decode

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.

~$12,000 MSRP

AMD Instinct MI250X 128GBAMD upgrade

128 GB VRAM (+120)3200 GB/s (+2944)

Makes the model fit on the accelerator instead of staying completely out of reach.38.8 tok/s decode

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.

~$15,000 MSRP

Frequently asked questions

Can RX 590 8GB run Qwen3.5 122B A10B?

No, Qwen3.5 122B A10B requires more memory than RX 590 8GB provides.

How much VRAM does Qwen3.5 122B A10B need?

Qwen3.5 122B A10B (122B parameters) requires approximately 75.8 GB of memory with Q3_K_M quantization.

What is the best quantization for Qwen3.5 122B A10B?

The recommended quantization for Qwen3.5 122B A10B is Q3_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 122B A10B run at on RX 590 8GB?

On RX 590 8GB, Qwen3.5 122B A10B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q3_K_M quantization.

Can RX 590 8GB run Qwen3.5 122B A10B for coding?

For coding workloads, Qwen3.5 122B A10B on RX 590 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Qwen3.5 122B A10B use on RX 590 8GB?

On RX 590 8GB, Qwen3.5 122B A10B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3.5 122B A10B feels slow on RX 590 8GB?

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 RX 590 8GB See all hardware for Qwen3.5 122B A10B

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