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.

Capabilities:

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

Inference speed

openchat 3.6 8b 20240522 IMat inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for openchat 3.6 8b 20240522 IMat 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 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

Opciones de mejora

Hardware que ejecuta bien openchat 3.6 8b 20240522 IMat

RX 7600 XT 16GBOpción económica

16 GB VRAM (+8)288 GB/s (+32)

Sube la velocidad estimada de decodificación alrededor de un 51%.34.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 51%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$329 MSRP

RX 9060 XT 16GBMejor relación calidad-precio

16 GB VRAM (+8)320 GB/s (+64)

Sube la velocidad estimada de decodificación alrededor de un 83%.41.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 83%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$349 MSRP

RX 7700 XT 12GBMejora AMD

12 GB VRAM (+4)432 GB/s (+176)

Sube la velocidad estimada de decodificación alrededor de un 135%.53.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 135%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$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.

See all results for RX 580 8GB See all hardware for openchat 3.6 8b 20240522 IMat

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