Can aya expanse 8b orthogonal heretic run on RX 6700 XT 12GB?

YES — Runs Great

C53Usable

Estimated from fit model

aya expanse 8b orthogonal heretic needs ~7.9 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 7.9 GB, 40.9 tok/s, Runs well

7.9 GB required12.0 GB available

66% VRAM used

Fit status

Runs well

Decode

40.9 tok/s

TTFT

4731 ms

Safe context

86K

Memory

7.9 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsaya expanse 8b orthogonal heretic on RX 6700 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: 40.9 tok/s decode · 4.7s TTFT (warm) · 102 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	40.9 tok/s	2581 ms	86K
Coding	C	Runs well	40.9 tok/s	4731 ms	86K
Agentic Coding	C	Runs well	40.9 tok/s	6882 ms	86K
Reasoning	C	Runs well	40.9 tok/s	5592 ms	86K
RAG	C	Runs well	40.9 tok/s	8603 ms	86K

Inference speed

aya expanse 8b orthogonal heretic inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for aya expanse 8b orthogonal heretic 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 orthogonal heretic (8B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C50
NVFP4	4	4.5 GB	Medium	C51
Q4_K_M	4	4.9 GB	Medium	C51
Q5_K_M	5	5.8 GB	High	C52
Q6_K	6	6.6 GB	High	C52
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run aya expanse 8b orthogonal heretic on your machine.

Run

lms load hf-mradermacher--aya-expanse-8b-orthogonal-heretic-gguf && lms server start

Frequently asked questions

Can RX 6700 XT 12GB run aya expanse 8b orthogonal heretic?

Yes, RX 6700 XT 12GB can run aya expanse 8b orthogonal heretic with a C grade (Runs well). Expected decode speed: 40.9 tok/s.

How much VRAM does aya expanse 8b orthogonal heretic need?

aya expanse 8b orthogonal heretic (8B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for aya expanse 8b orthogonal heretic?

The recommended quantization for aya expanse 8b orthogonal heretic is Q4_K_M, which balances quality and memory efficiency.

What speed will aya expanse 8b orthogonal heretic run at on RX 6700 XT 12GB?

On RX 6700 XT 12GB, aya expanse 8b orthogonal heretic achieves approximately 40.9 tokens per second decode speed with a time-to-first-token of 4731ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run aya expanse 8b orthogonal heretic for coding?

For coding workloads, aya expanse 8b orthogonal heretic on RX 6700 XT 12GB receives a C grade with 40.9 tok/s and 86K context.

What context window can aya expanse 8b orthogonal heretic use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, aya expanse 8b orthogonal heretic can safely use up to 86K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 6700 XT 12GB See all hardware for aya expanse 8b orthogonal heretic

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--aya-expanse-8b-orthogonal-heretic-gguf-on-rx-6700-xt-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

40.9 tok/s

2581 ms

86K

Coding

Runs well

40.9 tok/s

4731 ms

86K

Agentic Coding

Runs well

40.9 tok/s

6882 ms

86K

Reasoning

Runs well

40.9 tok/s

5592 ms

86K

RAG

Runs well

40.9 tok/s

8603 ms

86K

aya expanse 8b orthogonal heretic inference speed — tokens per second by GPU & Mac

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

Quantization options

How aya expanse 8b orthogonal heretic (8B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C50
NVFP4	4	4.5 GB	Medium	C51
Q4_K_M	4	4.9 GB	Medium	C51
Q5_K_M	5	5.8 GB	High	C52
Q6_K	6	6.6 GB	High	C52
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
F16	16	16.4 GB	Maximum	F0