Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

1,000 tok/s vs 85 tok/s visualized

dabit3's profile picture

nader dabit

191,201 subscribers

211,889 görüntüleme • 2 ay önce •via X (Twitter)

Bilim & TeknolojiEğitimOyun
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

This is awesome. Thanks Mia for this Was able to run with the RTX 3090 use this .env config DRAFT=mtp CONTEXT_SIZE=262144 CACHE_QUANT=8,4 GPU_MEM_GB=22 262K context: - Run 1: 65.98 tok/s - Run 2: 63.23 tok/s - Run 3: 64.41 tok/s Average: ~64.5 tok/s.
0:12

This is awesome. Thanks Mia for this Was able to run with the RTX 3090 use this .env config DRAFT=mtp CONTEXT_SIZE=262144 CACHE_QUANT=8,4 GPU_MEM_GB=22 262K context: - Run 1: 65.98 tok/s - Run 2: 63.23 tok/s - Run 3: 64.41 tok/s Average: ~64.5 tok/s.

Melvin Vivas

99,403 görüntüleme • 1 ay önce

Day-0 Qwen3.8-27B vs Qwen3.6-27B: the voxel pagoda test. Same prompt, one attempt each, single DGX Spark, both NVFP4. 3.8: 75,291 tok · 52m 56s · 23.7 tok/s (vLLM, native MTP) 3.6: 12,117 tok · 5m 13s · 38.8 tok/s (llama.cpp, DFlash K10) 3.8 thought for 160K characters before writing a line of code.
0:15

Day-0 Qwen3.8-27B vs Qwen3.6-27B: the voxel pagoda test. Same prompt, one attempt each, single DGX Spark, both NVFP4. 3.8: 75,291 tok · 52m 56s · 23.7 tok/s (vLLM, native MTP) 3.6: 12,117 tok · 5m 13s · 38.8 tok/s (llama.cpp, DFlash K10) 3.8 thought for 160K characters before writing a line of code.

Wësche

102,241 görüntüleme • 1 ay önce

10x speedup: OpenAI's privacy-filter on MLX vs CPU, side by side in your browser. 1,818 tok/s vs 179 tok/s. Real-time PII redaction across English, German, French. On-device. No cloud. Privacy-first. Which model should we port next?
0:15

10x speedup: OpenAI's privacy-filter on MLX vs CPU, side by side in your browser. 1,818 tok/s vs 179 tok/s. Real-time PII redaction across English, German, French. On-device. No cloud. Privacy-first. Which model should we port next?

Maziyar PANAHI

25,329 görüntüleme • 5 ay önce

NVIDIA just dropped Nemotron-3-Nano:4b — a tiny 2.8GB model. Guess whose hardware runs it the fastest? - RTX 4090: 226 tok/s - RTX 3090: 187 tok/s - Mac Studio M2 Ultra: 86 tok/s - Mac Mini M4: 25 tok/s Home court advantage is real. Also trying a new layout with live performance charts. Lmk what you think!
0:13

NVIDIA just dropped Nemotron-3-Nano:4b — a tiny 2.8GB model. Guess whose hardware runs it the fastest? - RTX 4090: 226 tok/s - RTX 3090: 187 tok/s - Mac Studio M2 Ultra: 86 tok/s - Mac Mini M4: 25 tok/s Home court advantage is real. Also trying a new layout with live performance charts. Lmk what you think!

stevibe

127,617 görüntüleme • 6 ay önce

RadixArkのQwen3.8-Flash-Next-NVFP4 つかってFreeTokenをcodexで改造してやっと良きスピード出た Activeが小さいからそれなりに速度出た RAM DDR5 8ch GPU - RTX5090 x1 Prefill 277.5 tok/s Decode 59.13 tok/s - RTX5090 x2 Prefill 619.5 tok/s Decode 83.14 tok/s
0:23

RadixArkのQwen3.8-Flash-Next-NVFP4 つかってFreeTokenをcodexで改造してやっと良きスピード出た Activeが小さいからそれなりに速度出た RAM DDR5 8ch GPU - RTX5090 x1 Prefill 277.5 tok/s Decode 59.13 tok/s - RTX5090 x2 Prefill 619.5 tok/s Decode 83.14 tok/s

ぱぷりか炒め

27,922 görüntüleme • 1 ay önce

One task, two models. GLM 5.3 Flash running slower at 25 tok/s prose Qwen3.8 Flash running faster at 47 tok/s prose Who will finish the task first?
0:14

One task, two models. GLM 5.3 Flash running slower at 25 tok/s prose Qwen3.8 Flash running faster at 47 tok/s prose Who will finish the task first?

Mia

27,452 görüntüleme • 27 gün önce

Ornith-1.0 35B - MLX Device - 128gb m5 max mbp Harness - PocketAI Hub 4 bit - 11,504 tokens, 95.98 tok/s, 21.20 GB peak RAM 8 bit - 13,128 tokens, 64.96 tok/s, 38.92 GB peak RAM BF16 - 12,491 tokens, 47.72 tok/s, 70.59 GB peak RAM prompt and link to model below
0:15

Ornith-1.0 35B - MLX Device - 128gb m5 max mbp Harness - PocketAI Hub 4 bit - 11,504 tokens, 95.98 tok/s, 21.20 GB peak RAM 8 bit - 13,128 tokens, 64.96 tok/s, 38.92 GB peak RAM BF16 - 12,491 tokens, 47.72 tok/s, 70.59 GB peak RAM prompt and link to model below

Trevor Wood

11,765 görüntüleme • 2 ay önce

Okay, I guess I didn't waste all that money after all Deepseek V4.1 Flash 304 tok/s code, 135 tok/s prose Zero optimization, will have it max optimized by the morning.
0:17

Okay, I guess I didn't waste all that money after all Deepseek V4.1 Flash 304 tok/s code, 135 tok/s prose Zero optimization, will have it max optimized by the morning.

Killy

44,653 görüntüleme • 24 gün önce

no one is ready for GPT-5.6 on Cerebras at 750 tok/s
0:13

no one is ready for GPT-5.6 on Cerebras at 750 tok/s

Kai Brokering

202,816 görüntüleme • 2 ay önce

Making a bop folder on tik tok dm if interested (『S』 will be included)
0:15

Making a bop folder on tik tok dm if interested (『S』 will be included)

bopperless

37,158 görüntüleme • 9 ay önce

Laguna XS 2.1 just matched Qwen 3.6 35B on a real coding task & ran twice as fast. > Same RTX 3090 > Laguna: 158 tok/s. Qwen: 81 tok/s The local model race isn't about size anymore. It's about who moves faster.
0:19

Laguna XS 2.1 just matched Qwen 3.6 35B on a real coding task & ran twice as fast. > Same RTX 3090 > Laguna: 158 tok/s. Qwen: 81 tok/s The local model race isn't about size anymore. It's about who moves faster.

0xMarioNawfal

63,839 görüntüleme • 2 ay önce

Two models. One DGX Spark One identical voxel Eiffel Tower prompt. Qwen3.8-27B xhigh: 83K tokens, 41m29s, 33.4 tok/s Ornith 1.5 35B-A3B: 47K tokens, 10m35s, 74.0 tok/s Thinking on, no token ceiling, matched camera. Ornith finished much faster; Qwen spent far more tokens.
0:18

Two models. One DGX Spark One identical voxel Eiffel Tower prompt. Qwen3.8-27B xhigh: 83K tokens, 41m29s, 33.4 tok/s Ornith 1.5 35B-A3B: 47K tokens, 10m35s, 74.0 tok/s Thinking on, no token ceiling, matched camera. Ornith finished much faster; Qwen spent far more tokens.

Wësche

51,527 görüntüleme • 1 ay önce

4 DGX Sparks running DeepSeek V4.1 Flash 6 concurrent sessions, 170.73 tok/s coding throughput. 🔥 1,200 output tokens in 7.028 seconds. Warm single-stream: 💻 Code: 77.61 tok/s 🧮 Math: 74.08 🧠 Reasoning: 48.75 ✍️ Prose: 33.02 6-stream aggregate: 💻 Code: 170.73 tok/s 🔢 Counting: 291.64 tok/s 552B MoE · MXFP4 · TP4 · vLLM + DSpark + CUDA graphs ✅ Tool calling + vision ✅ 127,480-token retrieval test passed ✅ 300K configured context Video replays the actual recorded streams at 1× speed. Thinking OFF, 200-token cap per coding request. Short-prompt coding throughput Credit to Tech2Wild for the Boot9 recipe. NVIDIA AI
0:10

4 DGX Sparks running DeepSeek V4.1 Flash 6 concurrent sessions, 170.73 tok/s coding throughput. 🔥 1,200 output tokens in 7.028 seconds. Warm single-stream: 💻 Code: 77.61 tok/s 🧮 Math: 74.08 🧠 Reasoning: 48.75 ✍️ Prose: 33.02 6-stream aggregate: 💻 Code: 170.73 tok/s 🔢 Counting: 291.64 tok/s 552B MoE · MXFP4 · TP4 · vLLM + DSpark + CUDA graphs ✅ Tool calling + vision ✅ 127,480-token retrieval test passed ✅ 300K configured context Video replays the actual recorded streams at 1× speed. Thinking OFF, 200-token cap per coding request. Short-prompt coding throughput Credit to Tech2Wild for the Boot9 recipe. NVIDIA AI

Wësche

24,282 görüntüleme • 24 gün önce

tik tok tik tok tik tok
0:18

tik tok tik tok tik tok

리버

120,332 görüntüleme • 1 yıl önce

Deepseek v4.1 Flash on 24GB Macbook. I think I reached my limit, 1.36 tok/s. It is far from being usable but the feeling of having a SOTA model on my consumer machine is awesome. I started from 0.74 tok/s and almost double it. It was a great journey.
0:15

Deepseek v4.1 Flash on 24GB Macbook. I think I reached my limit, 1.36 tok/s. It is far from being usable but the feeling of having a SOTA model on my consumer machine is awesome. I started from 0.74 tok/s and almost double it. It was a great journey.

Marco Franzon

90,851 görüntüleme • 21 gün önce

MiniMax M2.5 casually responding at ~50 tok/s with MLX (M3 Ultra). The model was released one hour ago 🥳
0:14

MiniMax M2.5 casually responding at ~50 tok/s with MLX (M3 Ultra). The model was released one hour ago 🥳

Pedro Cuenca

170,567 görüntüleme • 7 ay önce

tok tok tok ako'y kumakatok, ha ha ha ako'y natatawa
0:18

tok tok tok ako'y kumakatok, ha ha ha ako'y natatawa

zico

14,963 görüntüleme • 3 yıl önce

❄️🐰’s on Tik Tok are going viral after doing this trend 🤭 Don’t let envious Ethan see this 😭👑
0:11

❄️🐰’s on Tik Tok are going viral after doing this trend 🤭 Don’t let envious Ethan see this 😭👑

#ReclaimD1

29,421 görüntüleme • 6 ay önce

The original Tik Tok challenge Vs The version of black women
0:16

The original Tik Tok challenge Vs The version of black women

𝑻𝒉𝒆 𝑺𝒂𝒍𝒕 𝑶𝒇 𝑻𝒉𝒆 𝑬𝒂𝒓𝒕𝒉

744,550 görüntüleme • 2 yıl önce