Best quality at ≥ 5 t/s
K2-Horizon-MoVA-36B-A4B
25.3 AA index single mode
Q4_K_Mllama.cpp CPUCPU only
~6.2–7.3 t/s Estimated extrapolated
Speed extrapolated, not measured on this setup.
Intel · Accelerator
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
No GPU (CPU only)64 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
25.3 AA index single mode
Q4_K_Mllama.cpp CPUCPU only
~6.2–7.3 t/s Estimated extrapolated
Speed extrapolated, not measured on this setup.
Faster tradeoff
18.2 AA index Reasoning
Q4_K_Mllama.cpp CPUCPU only
~11–13 t/s Estimated extrapolated
Next point on the quality/speed front; then MiniCPM5-2B.
Possibly better · speed not established
No higher-scoring model has a feasible plan here.
Best plan per model that reaches your floor by a matching measurement, a calibrated range, or an extrapolated estimate (labelled). Sorted by the selected quality metric, then speed.
IFM/K2-Horizon-MoVA-36B-A4B
Qwen/Qwen3.6-35B-A3B
IFM/K2-Horizon-3.7B
Qwen/Qwen3.5-4B
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
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
ai9stars/G9v3-3B
ibm-granite/granite-4.2-3b
LiquidAI/LFM2.5-2.6B
LiquidAI/LFM2.5-8B-A1B
Qwen/Qwen3.5-2B
Qwen/Qwen3.5-0.8B
LiquidAI/LFM2.5-1.2B-Thinking
LiquidAI/LFM2.5-1.2B-Instruct
LiquidAI/LFM2.5-VL-1.6B
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
IFM/K2-Horizon-0.9B
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.
google/gemma-4-26B-A4B-it
meituan-longcat/LongCat-Flash-Lite
inclusionAI/Ling-3.0-tiny
Qwen/Qwen3-Next-80B-A3B-Instruct
google/diffusiongemma-26B-A4B-it
Qwen/Qwen3-Coder-Next
google/gemma-4-E2B-it
meta-llama/Llama-3.3-70B-Instruct
microsoft/Phi-4-mini-instruct
CohereLabs/tiny-aya-global
No measurement, calibration or bandwidth figure for this plan.
openai/gpt-oss-20b
ibm-granite/granite-4.0-h-1b
ibm-granite/granite-4.0-micro
ibm-granite/granite-4.0-350m
ibm-granite/granite-4.0-h-350m
Fits, but the best evidence (measured, estimated or the bandwidth ceiling) is under your floor. Lower the floor or add memory bandwidth.
Qwen/Qwen3.8-27B
IFM/K2-Horizon-7B
meta-models/Muse-Glimmer-30B
google/gemma-4-31B-it
google/gemma-4-12B-it
LGAI-EXAONE/EXAONE-4.5-33B
ibm-granite/granite-4.2-30b
Qwen/Qwen3.5-9B
ibm-granite/granite-4.2-8b
trillionlabs/Tri-21B-Think
google/gemma-4-E4B-it
allenai/Olmo-3.1-32B-Think
allenai/Olmo-3.1-32B-Instruct
ibm-granite/granite-4.0-h-small
microsoft/phi-4
allenai/Olmo-3-7B-Think
allenai/Olmo-3-7B-Instruct
swiss-ai/Apertus-70B-Instruct-2509
allenai/Molmo2-8B
swiss-ai/Apertus-8B-Instruct-2509
| Memory | 128 GB HBM2e | source |
|---|---|---|
| Bandwidth | 3,700 GB/s | source |
| FP16 | unknown | No unambiguous comparable value verified; never substitute AI TOPS, BF16, or whole-system power. |
| Power | 600 W | source |
0 observations
No reported observation resolved to this exact device yet.
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