Ahmad
@TheAhmadOsman • 80,581 subscribers
Founder and CEO, @OsmanticAI — Accelerating Local AI Adoption • I moderate GPUs on r/LocalLLaMA
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INCREDIBLE SPEED running Claude Code w/ local models on my own GPUs at home > SGLang serving MiniMax-M2.1 > on 8x RTX 3090s > nvtop showing live GPU load > Claude Code generating code + docs > end-2-end on my AI cluster MiniMax-M2.1 is my favorite model to run locally nowadays
Ahmad593,078 views • 9 months ago
running Claude Code w/ local models on my own GPUs at home > vLLM serving GLM-4.5 Air > on 4x RTX 3090s > nvtop showing live GPU load > Claude Code generating code + docs > end-to-end on my AI cluster this is what local AI actually looks like Buy a GPU
Ahmad262,712 views • 10 months ago
running Qwen3.5 397B MoE (17B active/token) on 4x DGX Sparks in FP8 (~400GB) > OpenCode driving > agent exploring its own config > probing all 4 Sparks (via ssh) + reporting thermals > inspecting how vLLM is serving it > collecting + analyzing its own stats local AI is awesome
Ahmad122,543 views • 7 months ago
MiniMax M2.7 at home running on 4x DGX Sparks vLLM serving full BF16 weights, 200k context OpenCode having the model monitor its own hardware and report thermals, tokens/sec, TTFT, and other runtime stats in real time What benchmarks / workflows / things do you wanna see next?
Ahmad92,491 views • 6 months ago
How Fast is Gemma 4 on a MacBook Pro M4? Benchmarking Google's new MoE (26B-A4B) > Model size: 26.1 GiB > Load time: ~4.2s Comparing single request VS > concurrent requests performance > 32k total context, 4 parallel slots single request behavior > TTFT: 5.68s > prompt: 3,701 tokens @ 652 tok/s > decode: 40.08 tok/s sequential (1 request at a time): > avg duration: 20.5s > p99: 22.1s > throughput: 40.11 tok/s > clean finishes: 100% concurrent (4 parallel requests): > aggregate throughput: 47.25 tok/s > total system throughput: 262.27 tok/s > avg duration: 65.1s > p95 latency: 68.8s > req/sec: 0.058 Head-to-Head: Sequential vs Concurrent throughput: > 40.11 tok/s → 47.25 tok/s (+17.8%) > small gain despite 4x parallelism latency per request: > 20.5s → 65.1s (~3.2x slower) > you pay heavily for concurrency system throughput (true utilization): > ~40 tok/s → 262 tok/s (~6.5x total output) > this is where concurrency wins tokens per second (decode ceiling): > ~40 tok/s steady in both modes > hardware-bound, not scheduler-bound TTFT impact: > ~5.7s baseline → buried under queueing in concurrent > “headers waittime” becomes the bottleneck What this actually means? - You don’t get linear scaling from parallel slots - You trade latency for total output - Mac Unified Memory setup is clearly saturating - Bandwidth + Scheduling overhead show up immediately This is exactly why GPUs dominate here Concurrency without killing latency
Ahmad88,866 views • 7 months ago
While everyone is talking about GPT-5.4 Thinking and GPT-5.4 Pro I wanna remind you that I am GIVING AWAY this $15,000 GPU So you can run your AI at home instead of sending your data to OpenAI, Anthropic, etc COMPLETELY FREE Take a min to sign up below & this could be yours
Ahmad91,558 views • 8 months ago
Impressive demo running on a single box The future of AI is local btw
Ahmad40,324 views • 6 months ago
GPU GIVEAWAY Buy a GPU × GTC 2026 = Give a GPU > RTX PRO 6000 Blackwell > 96GB VRAM • NVFP4 > ~$15K value > Brand new If this does well I’ll ask NVIDIA for more GPUs next time… maybe even DGX Sparks How to enter? Short clip here Full clip in the replies GO GO GO
Ahmad36,857 views • 8 months ago
Went on David Ondrej’s show to talk Opensource and Local AI Every $ sent to an API helps them buy GPUs you can no longer own, open models are catching the frontier, orgs are cutting cloud costs ~70%, ODS is the local stack in one command, and the endgame is simple: own the stack
Ahmad11,737 views • 2 months ago
