moonshotai/Kimi-K3
Image-Text-to-Text • 2.8T • Updated • 2.85k • • 5.74k
Mitko Vasilev
mitkox
AI & ML interests
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
Recent Activity
liked a model about 10 hours ago
moonshotai/Kimi-K3 updated a model 6 days ago
mitkox/antares-1b-Q8_0-GGUF published a model 6 days ago
mitkox/antares-1b-Q8_0-GGUFOrganizations
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posted an update 5 months ago
Post
6022
My USB charger has a Blackwell GPU and 128GB RAM.
What. A. Time. To. Be. Alive.
People in Sofia: “It’s freezing.”
Me: sitting next to 3kW of space AI heaters on my desk 👀
1x GLM-5, 2x MiniMax-M2.5, 1x Qwen3 Coder Next; all on single Aibrix/K8s cluster
What. A. Time. To. Be. Alive.
People in Sofia: “It’s freezing.”
Me: sitting next to 3kW of space AI heaters on my desk 👀
1x GLM-5, 2x MiniMax-M2.5, 1x Qwen3 Coder Next; all on single Aibrix/K8s cluster
- 6 replies
posted an update 5 months ago
Post
885
134,614 tok/sec input prefil max
1031 tokens/sec out gen max
At these local AI speeds, there is no User Interface for humans. My human UI is the Radicle distributed Git issues queue
On my GPU workstation:
- Z8 Fury G5 4x A6000
- MiniMax-M2.5
- Claude Code to localhost:8000
1031 tokens/sec out gen max
At these local AI speeds, there is no User Interface for humans. My human UI is the Radicle distributed Git issues queue
On my GPU workstation:
- Z8 Fury G5 4x A6000
- MiniMax-M2.5
- Claude Code to localhost:8000
- 1 reply
posted an update 6 months ago
Post
4891
I just pushed Claude Code Agent Swarm with 20 coding agents on my desktop GPU workstation.
With local AI, I don’t have /fast CC switch, but I have /absurdlyfast:
- 100’499 tokens/second read, yeah 100k, not a typo | 811 tok/sec generation
- KV cache: 707’200 tokens
- Hardware: 5+ year old GPUs 4xA6K gen1; It’s not the car. It’s the driver.
Qwen3 Coder Next AWQ with cache at BF16. Scores 82.1% in C# on 29-years-in-dev codebase vs Opus 4.5 at only 57.5%. When your codebase predates Stack Overflow, you don't need the biggest model; you need the one that actually remembers Windows 95.
My current bottleneck is my 27" monitor. Can't fit all 20 Theos on screen without squinting.
With local AI, I don’t have /fast CC switch, but I have /absurdlyfast:
- 100’499 tokens/second read, yeah 100k, not a typo | 811 tok/sec generation
- KV cache: 707’200 tokens
- Hardware: 5+ year old GPUs 4xA6K gen1; It’s not the car. It’s the driver.
Qwen3 Coder Next AWQ with cache at BF16. Scores 82.1% in C# on 29-years-in-dev codebase vs Opus 4.5 at only 57.5%. When your codebase predates Stack Overflow, you don't need the biggest model; you need the one that actually remembers Windows 95.
My current bottleneck is my 27" monitor. Can't fit all 20 Theos on screen without squinting.
- 3 replies
posted an update 6 months ago
Post
396
▐▛██▜▌ Claude Code v2.1.23
▝████▘ Kimi-K2.5 · API Usage Billing
▘▘ ▝▝ ~/dev/vllm
/model to try Opus 4.5
❯ hey
● Hello! How can I help you today?
❯ what model are you?
● I'm Claude Kimi-K2.5, running in a local environment on Linux.
Took some time to download and vLLM hybrid inferencing magic to get it running on my desktop workstation.
▝████▘ Kimi-K2.5 · API Usage Billing
▘▘ ▝▝ ~/dev/vllm
/model to try Opus 4.5
❯ hey
● Hello! How can I help you today?
❯ what model are you?
● I'm Claude Kimi-K2.5, running in a local environment on Linux.
Took some time to download and vLLM hybrid inferencing magic to get it running on my desktop workstation.
posted an update 6 months ago
Post
1624
GLM-4.7-Flash is fast, good and cheap.
3,074 tokens/sec peak at 200k tokens context window on my desktop PC.
Works with Claude Code and opencode for hours. No errors, drop-in replacement of the Anthropic cloud AI.
MIT licensed, open weights, free for commercial use and modifications.
Supports speculative decoding using MTP, which is highly effective in mitigating latency.
Great for on device AI coding as AWQ 4bit at 18.5 GB. Hybrid inference on a single consumer GPU + CPU RAM.
3,074 tokens/sec peak at 200k tokens context window on my desktop PC.
Works with Claude Code and opencode for hours. No errors, drop-in replacement of the Anthropic cloud AI.
MIT licensed, open weights, free for commercial use and modifications.
Supports speculative decoding using MTP, which is highly effective in mitigating latency.
Great for on device AI coding as AWQ 4bit at 18.5 GB. Hybrid inference on a single consumer GPU + CPU RAM.
- 3 replies