AMD · consumer

Radeon RX 9070 XT

Radeon RX 9070 XT has 16 GB of VRAM at 640 GB/s — about 14.88 GiB usable after driver and compositor overhead. 1856 of 2118 indexed models fit at 8K context with q8_0 KV.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
16 GB
GDDR6
Bandwidth
640 GB/s
256-bit bus
Tensor FP16
dense
TDP
304 W
$599 MSRP
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
text 1591vision language 162video 15embedding 26audio asr 39audio tts 21image 2

What fits at 8K context

largest quantization that fits, per model · 1856 of 2118 indexed
ModelBest quantParamsWeightsKVTotal in memoryHeadroomtok/s
Qwen3-Coder-30B-A3B-InstructMoEQ3_K_L30.5B13.58 GiB0.40 GiB14.87 GiB0.01 GiB100±37%
Qwen3-VL-30B-A3B-ThinkingMoEQ3_K_L31.1B13.58 GiB0.40 GiB14.87 GiB0.01 GiB100±37%
MiroThinker-v1.0-30BMoEQ3_K_L30.5B13.58 GiB0.40 GiB14.87 GiB0.01 GiB100±37%
Qwen3-30B-A3BMoEQ3_K_L30.5B13.58 GiB0.40 GiB14.87 GiB0.01 GiB100±37%
Pantheon-Proto-RP-1.8-30B-A3BMoEQ3_K_L30.5B13.58 GiB0.40 GiB14.87 GiB0.01 GiB100±37%
Tongyi-DeepResearch-30B-A3BMoEQ3_K_L30.5B13.58 GiB0.40 GiB14.87 GiB0.01 GiB100±37%
internlm2-math-plus-20bI1-Q5_K_M19.9B13.11 GiB0.80 GiB14.87 GiB0.01 GiB29±26.5%
Muse-Glimmer-30BQ3_K_L29.8B13.77 GiB0.10 GiB14.85 GiB0.03 GiB29±26.5%
command-r-35b-writer-v2I1-IQ1_M35.0B8.52 GiB5.31 GiB14.84 GiB0.04 GiB29±26.5%
Darwin-35B-A3B-OpusMoEIQ3_XXS36.0B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
Aurora-Code-1MoEIQ3_XXS34.7B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
granite-20b-code-instruct-8kQ5_K_L20.1B13.86 GiB0.00 GiB14.84 GiB0.04 GiB29±26.5%
grug-35b-v2MoEIQ3_XXS35.1B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
grug-35bMoEIQ3_XXS35.1B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
WorldSim-Opus-3.6-35B-A3BMoEIQ3_XXS35.1B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
Qwen3.6-35B-A3B-AnkoMoEIQ3_XXS35.1B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
KAT-Coder-V2.5-DevMoEIQ3_XXS34.7B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
Ornith-1.0-35BMoEIQ3_XXS34.7B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
Nex-N2-miniMoEIQ3_XXS35.1B13.85 GiB0.08 GiB14.84 GiB0.04 GiB142±37%
ThinkingCap-Qwen3.6-27BQ3_K_M27.4B13.60 GiB0.27 GiB14.83 GiB0.05 GiB29±26.5%
Tess-4-27BQ3_K_M27.8B13.60 GiB0.27 GiB14.83 GiB0.05 GiB29±26.5%
gpt-oss-20b-hereticMoEQ4_K_S20.9B13.83 GiB0.11 GiB14.83 GiB0.05 GiB81±37%
IQuest-Coder-V1-40B-InstructI1-IQ2_M39.8B12.50 GiB1.33 GiB14.83 GiB0.05 GiB29±26.5%
CallerIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Dumpling-Qwen2.5-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
OREAL-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
QwQ-32B-Preview-abliterated-linear25I1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
openhands-lm-32b-v0.1I1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-Coder-32B-abliteratedI1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
m1-32bI1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
XMainframe-v2-Instruct-32bI1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-Coder-32B-Python-SpecialistI1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-32b-RP-InkI1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
LongWriter-Zero-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
OpenCodeReasoning-Nemotron-32B-IOIIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-Coder-32B-Instruct-abliteratedIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
OlympicCoder-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
OpenCodeReasoning-Nemotron-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
OpenThinker-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
QwQ-32B-ArliAI-RpR-v4IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-Coder-32B-InstructIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-Coder-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
QwQ-32B-abliteratedIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
DeepSeek-R1-Distill-Qwen-32B-hereticI1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
InnoSpark-HPC-RM-32BI1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
OpenThinker2-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
INTELLECT-2IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-32B-InstructIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-Coder-32B-Instruct-UncensoredI1-IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
QwQ-32B-PreviewIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
TinyR1-32B-PreviewIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
deepseek-r1-qwen-2.5-32B-ablatedIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Rombos-LLM-V2.5-Qwen-32bIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
DeepSeek-R1-Distill-Qwen-32B-abliteratedIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-32B-ArliAI-RPMax-v1.3IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
DeepSeek-R1-Distill-Qwen-32BIQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
Qwen2.5-VL-32B-InstructIQ3_XS33.5B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
EVA-Qwen2.5-32B-v0.2IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
EVA-Qwen2.5-32B-v0.1IQ3_XS32.8B12.76 GiB1.06 GiB14.83 GiB0.05 GiB29±26.5%
MN-GRAND-23.5B-Gutenberg-UNCENSORED-V2-GLM4.7-ThinkingI1-Q4_K_S23.4B12.53 GiB1.34 GiB14.82 GiB0.06 GiB29±26.5%
From the filePredictedwhat these mean

Speed is modeled, not measured: decode is memory-bandwidth bound, so tokens per second is bytes read per token against achievable bandwidth. Mixture-of-experts models carry a wider band because only the routed experts are read each step, and few have been measured publicly.

Measured on this card

third-party benchmarks, aggregated
WorkloadMedianMiddle 50%Runs
Image generation5.76 it/s2.5411.88118
Prompt processing4275.83 tok/s3591.414809.8730
Text generation95.24 tok/s86.48108.0130
Benchmarked· n=118

Aggregated from community-submitted runs, so the spread is wide by nature — it covers different models, resolutions, step counts and settings, not one controlled configuration. Read the middle 50% rather than the median alone. These figures are reproduced with attribution from vladmandic-sd-data-benchmark, which publishes no licence — so we display and link rather than redistribute them.

Questions people ask

What AI models can a Radeon RX 9070 XT run?
1856 of 2118 indexed open-weight models fit a Radeon RX 9070 XT at 8,192 context with q8_0 KV cache, the largest being Qwen3-Coder-30B-A3B-Instruct at Q3_K_L. That covers text, vision-language, image, video and speech models.
How much usable memory does a Radeon RX 9070 XT actually have?
Its nameplate is 16 GB, but about 14.88 GiB is available to a model once driver and compositor overhead is accounted for.
Is a Radeon RX 9070 XT fast for local AI?
Its memory bandwidth is 640 GB/s, and that figure — not teraflops — is what governs token generation speed. Capacity decides what you can run; bandwidth decides how fast it runs.