NVIDIA Jetson AGX Thor 128GB

reported speed:

36.7 tokens/s generation · 899.0 tokens/s prompt processing

quant:

NVFP4 (NVFP4)

kv:

int8

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports Qwen3.8-Flash-Next NVFP4 at 36.7 tok/s one-user decode and 899 tok/s prefill at 32k on a Jetson AGX Thor 128GB. Setup is TensorFold 0.6.0 with int8 KV cache, full 262,144-token window, --parallel 4. Prefill measured at 926 / 899 / 798 tok/s for 8k / 32k / 128k tokens; combined decode at 1 / 2 / 3 / 4 users is 34.8 / 53.7 / 70.3 / 76.7 tok/s; first answer to a 60k-token prompt takes 69.7 s. User compares against vLLM's FP8 build on the same box (38.4 tok/s decode, 2,101 tok/s prefill at 32k) and describes a local Thor prompt path with gated runs r09 and r11 reaching 1,549 tok/s prefill at 32k. 128 GB and 273 GB/s are published Thor specs, not re-measured.

reported speed:

25.0 tokens/s generation · 3557.0 tokens/s prompt processing

quant:

NVFP4 (NVFP4)

kv:

8-bit

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User benchmarks Qwen3.8-27B at 24.96 t/s generation and 3557 t/s prompt processing on a Jetson AGX Thor 128GB, using the Mjolnir vLLM image with the FA4 GEMV decode kernel. Setup is vLLM 0.30.0 with NVFP4 weights and an 8-bit KV cache at 8K context, single request. The GEMV kernel is default-on and dispatches only on M=1, head_dim=256, GQA shapes. The GEMV leg wins at c=1 but regresses at c=4 (57.74 t/s at 8K context, −15.4% versus stock vLLM), which the user attributes to an open investigation. Stock vLLM reaches 24.43 t/s and 2468 t/s prompt at the same setting.

reported speed:

15.3-18.5 tokens/s generation · 90-170 tokens/s prompt processing

quant:

Q4_K_XL (GGUF)

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports Qwen3.8-Flash-Next running on a Jetson AGX Thor 128GB with vision support, decoding at 15.3-18.5 tok/s on free-form text. Setup is llama.cpp (qwen4exp branch, commit d4a943f plus cherry-pick 24ea62d and canreuse-v2.patch) with UD-Q4_K_XL GGUF, 65536 context, ngram-mod speculative decoding, and the 51B n-gram table offloaded to CPU/NVMe via -ot per_layer_token_embd=CPU -lm mmap, leaving about 80GB resident. Prefill is 90-170 tok/s depending on caching; ngram-mod speculation reached 82.9 tok/s on verbatim code reproduction (91% draft acceptance, mean accepted span 59 tokens) but only fires on long untouched spans. The 120W power mode costs about 10% versus MAXN, and images cost a one-off 1-2s to encode without affecting decode speed.

reported speed:

139.1 tokens/s generation

quant:

NVFP4 (NVFP4)

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports Qwen3.6-35B-A3B-NVFP4 at 139.1 tok/s on a single NVIDIA Jetson AGX Thor with 117 GB unified memory. Setup is vLLM built from source for sm_110a with DFlash speculative decoding (12 tokens), marlin MoE backend, flash_attn attention backend, 65536 context length, and 0.78 GPU memory utilization. The post also benchmarks Qwen3.5-4B-NVFP4 at 155.8 tok/s, Qwen3.6-27B-NVFP4 at 50.1 tok/s, and Qwen3.5-122B-A10B-NVFP4 at 52.6 tok/s, all at concurrency 1. The 122B requires cutlass MoE and TRITON_ATTN due to a Marlin crash at 256 experts, and achieves 27-42 tok/s with DFlash versus 10.9 tok/s autoregressive.