Best quality at ≥ 5 t/s
No plan with a quality score reaches 5 t/s on this setup.
AMD · Multi-GPU module
GPUs
Unified memory
System RAM
≈ 89.6 GB/s theoretical ceiling (MT/s × 8 bytes × channels). DDR5’s two 32-bit subchannels count as one channel.
CPU
Used for CPU layers/experts and the -t flag. A reference platform fills its maximum channels and speed; set what you actually populated.
Defaults are editorial planning choices, not measurements: 1 GiB runtime + 0.5 GiB margin per device, 75% of unified memory, 8 GiB RAM for the OS, a vendor “24 GB” treated as 24 GiB (enter your driver’s available VRAM to override), 5 t/s minimum speed (research editorial default). Memory ledger
2× Instinct MI25064 GB DDR5-5600 ×2 ch · 56.0 GiB usable · 89.6 GB/s theoretical8K contextF16 KVany backend≥ 5 t/s
Best quality at ≥ 5 t/s
No plan with a quality score reaches 5 t/s on this setup.
Faster tradeoff
No faster plan with a quality score.
Possibly better · speed not established
39.8 AA index single mode
Q4_K_Mllama.cpp ROCmLayer split across GPUs
speed Unknown
No report, calibration or bandwidth figure for this setup. Also: Qwen3.8-27B, NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16.
The weights fit, but the KV cache for this attention type (or the chosen cache layout) is not established, so a fit cannot be guaranteed.
inclusionAI/Ling-3.0-flash-VL
inclusionAI/Ling-3.0-flash-Fin
google/gemma-4-26B-A4B-it
google/gemma-4-31B-it
google/gemma-4-12B-it
meituan-longcat/LongCat-Flash-Lite
mistralai/Mistral-Small-4-119B-2603
inclusionAI/Ling-3.0-tiny
Qwen/Qwen3-Next-80B-A3B-Instruct
google/diffusiongemma-26B-A4B-it
Qwen/Qwen3-Coder-Next
google/gemma-4-E4B-it
tiiuae/Falcon-H1R-7B
google/gemma-4-E2B-it
meta-llama/Llama-3.3-70B-Instruct
meta-llama/Llama-4-Scout-17B-16E-Instruct
microsoft/Phi-4-mini-instruct
CohereLabs/tiny-aya-global
No measurement, calibration or bandwidth figure for this plan.
Qwen/Qwen3.8-Flash-Next
Qwen/Qwen3.8-27B
nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16
thinkingmachines/Inkling-Small
IFM/K2-Horizon-MoVA-36B-A4B
tencent/Hy3
ai9stars/G9v3-39A5B
nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16
IFM/K2-Horizon-7B
Qwen/Qwen3.6-35B-A3B
meta-models/Muse-Glimmer-30B
IFM/K2-Horizon-3.7B
Qwen/Qwen3.5-122B-A10B
mistralai/Mistral-Medium-3.5-128B
LGAI-EXAONE/EXAONE-4.5-33B
Qwen/Qwen3.5-4B
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
ibm-granite/granite-4.2-30b
openbmb/MiniCPM5-2B
MultiverseComputingCAI/Hypernova-60B-2605
Also: HyperNova-60B (listed with its dated update); Hypernova-60B-2602 (dated update, not separately evaluated)
nvidia/Nemotron-Cascade-2-30B-A3B
openai/gpt-oss-120b
Qwen/Qwen3.5-9B
ibm-granite/granite-4.2-8b
ai9stars/G9v3-3B
nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
CohereLabs/North-Mini-Code-1.0
ibm-granite/granite-4.2-3b
trillionlabs/Tri-21B-Think
openai/gpt-oss-20b
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
openbmb/MiniCPM5-1B
Nanbeige/Nanbeige4.1-3B
LiquidAI/LFM2.5-2.6B
stepfun-ai/Step3-VL-10B
nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16
LiquidAI/LFM2.5-8B-A1B
allenai/Olmo-3.1-32B-Think
Qwen/Qwen3.5-2B
allenai/Olmo-3.1-32B-Instruct
Qwen/Qwen3.5-0.8B
ibm-granite/granite-4.0-h-small
microsoft/phi-4
allenai/Olmo-3-7B-Think
allenai/Olmo-3-7B-Instruct
LiquidAI/LFM2.5-1.2B-Thinking
LiquidAI/LFM2.5-1.2B-Instruct
ibm-granite/granite-4.0-h-1b
swiss-ai/Apertus-70B-Instruct-2509
ibm-granite/granite-4.0-micro
allenai/Molmo2-8B
swiss-ai/Apertus-8B-Instruct-2509
ibm-granite/granite-4.0-350m
ibm-granite/granite-4.0-h-350m
LiquidAI/LFM2.5-VL-1.6B
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
IFM/K2-Horizon-0.9B
| Memory | 128 GB HBM2e | source |
|---|---|---|
| Bandwidth | 3,200 GB/s | source |
| FP16 | 362.1 TFLOPS | source |
| Power | 560 W | source |
FP16: dense_matrix_fp16. Power: module peak TDP;500W standard/560W peak.
0 observations
No reported observation resolved to this exact device yet.
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