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LLM fit topic

Find the best local LLM for your hardware in under a minute

This topic page turns the llmfit-style hardware-fit workflow into a browser experience. Pick your VRAM, context target, and goal, then get a shortlist of local models that actually fit.

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LLM fit topic

LLM Fit Finder for Local Models

Use the CCJK LLM Fit Finder to match your GPU VRAM, context budget, and workload to local AI models. Inspired by llmfit and built for web-first discovery.

What this page does

Instead of dumping another generic model list, this page starts from the hardware you actually have and points you to open-weight models that are realistic for local deployment.

View llmfit on GitHub
These recommendations are for quick shortlist generation, not exact benchmark guarantees.

Quick hardware fit

12 GB

Primary goal

Fast presets

Recommended local models

Qwen 2.5 Coder 14B Instruct

CCJK local fallback matcher
12 GB · 32K · Balanced setup
Official llmfit CLI is not available on this server yet, so results are generated by the CCJK fallback matcher.

#1 · Alibaba Cloud

Qwen 2.5 Coder 14B Instruct

One of the most practical local coding-first models for single-GPU workstations.

100
Best fit
12-16 GB VRAM128K contextQ4_K_MOllama, llama.cpp, or vLLM

This model should fit on 12GB with Q4_K_M quantization.

Its main strength aligns with your coding goal.

It supports up to 128K context, covering your 32K target.

Recommended stack: Ollama, llama.cpp, or vLLM.

Best for

Local coding, repo Q&A, patch generation, coding copilots.

Avoid if

You only have sub-10GB VRAM available.

#2 · DeepSeek

DeepSeek R1 Distill Qwen 14B

A strong reasoning-leaning local model for step-by-step answers and structured tasks.

100
Best fit
12-16 GB VRAM64K contextQ4_K_MOllama, llama.cpp, or vLLM

This model should fit on 12GB with Q4_K_M quantization.

Its main strength aligns with your coding goal.

It supports up to 64K context, covering your 32K target.

Recommended stack: Ollama, llama.cpp, or vLLM.

Best for

Planning, research, reasoning-heavy coding support, chain-of-thought style tasks.

Avoid if

You need the fastest interactive chat latency.

#3 · Microsoft

Phi-4 Mini Instruct

A compact model with stronger reasoning than most small-footprint local options.

90
Best fit
5-7 GB VRAM128K contextQ4_K_MOllama or llama.cpp

Your 12GB budget gives this model comfortable VRAM headroom.

Reasoning strength still helps with debugging, planning, and code review flows.

It supports up to 128K context, covering your 32K target.

Recommended stack: Ollama or llama.cpp.

Best for

Portable reasoning, local note-taking, low-cost experimentation.

Avoid if

You want the strongest code generation for production workflows.

#4 · Alibaba Cloud

Qwen 2.5 7B Instruct

A balanced multilingual model with broad capability and solid local latency.

88
Best fit
6-8 GB VRAM128K contextQ4_K_MOllama, llama.cpp, or vLLM

Your 12GB budget gives this model comfortable VRAM headroom.

It is better for chat, multilingual, agents than for coding.

It supports up to 128K context, covering your 32K target.

Recommended stack: Ollama, llama.cpp, or vLLM.

Best for

General-purpose assistants, multilingual teams, lightweight agent chains.

Avoid if

You mostly optimize for code-heavy tasks on larger GPUs.

#5 · Mistral AI

Mistral Nemo 12B

A strong mid-range local model for multilingual chat and fast assistant experiences.

88
Best fit
10-12 GB VRAM128K contextQ4_K_MOllama, llama.cpp, or vLLM

Your 12GB budget gives this model comfortable VRAM headroom.

It is better for chat, multilingual, agents than for coding.

It supports up to 128K context, covering your 32K target.

Recommended stack: Ollama, llama.cpp, or vLLM.

Best for

Fast local chat, support tooling, multilingual copilots.

Avoid if

You want the best possible code synthesis per token.

How we map hardware to models

We score each model by VRAM fit, context coverage, and whether its strengths match your goal such as coding, reasoning, or multilingual work.
We then bias the ranking based on your priority: faster response, balanced setup, or highest quality output.
The result is a shortlist you can act on immediately, with suggested runtimes like Ollama, llama.cpp, or vLLM.

FAQ

Is this the official llmfit UI?

No. This is a CCJK web experience inspired by llmfit’s hardware-fit approach, built so users can discover suitable local models without leaving the site.

Why ask for VRAM instead of auto-detecting my machine?

A public website cannot reliably inspect a visitor’s local GPU stack. Manual input is the safest way to keep the tool fast, privacy-friendly, and indexable.

When should I still use a hosted API provider?

If you need top-tier coding quality, very long context, or zero ops overhead, a hosted provider is usually the better choice. Use this tool for local-first and hybrid paths.

Need a hosted option instead?

Compare local-first candidates with API providers, model directories, and coding tool rankings.

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