# The Local LLM Matrix: Best Models & Quants by VRAM Tier (<=16GB – 256GB+)

> Source: <https://forum.level1techs.com/t/the-local-llm-matrix-best-models-quants-by-vram-tier-16gb-256gb/256443#post_2>
> Published: 2026-09-18 03:03:01+00:00

# 

Rather than running rigid polls right out of the gate, this thread is designed to gather real-world, operational data from the community. Automated benchmarks and leaderboards miss how setups actually behave day-to-day - I want to know what you are actually loading up, what you use it for, and how you have it tuned.

Use this space to share your daily drivers, niche workflow solutions, hardware limits, and suggestions on how to improve this living guide.

## 

Whether you are pushing multi-GPU tensor parallelism in headless servers or running daily local workflows on consumer cards and unified memory laptops, chime in on:

- **What model & quantization do you actually rely on?** (e.g., AutoRound W4A16, GGUF Q4_K_M/IQ4_XS, native FP8, unquantized BF16).
- **What is your primary use case?** (Agentic coding, large repo ingestion, technical RAG, multi-step math/logic, casual chat, long-form creative writing).
- **How are you serving it?**  - Backend / runtime: **vLLM** , direct**llama.cpp** ,**LM Studio** ,**Ollama** ,**Unsloth Desktop** , etc.
  - Architecture notes: Single GPU, multi-GPU split (tensor parallel or row/layer offloading), host RAM offloading, custom RoPE/YaRN factors, or speculative decoding.
- **Context & KV behavior:** Did you keep the KV cache unquantized (FP16/BF16), or did you drop to FP8/FP4? Where does the model hold coherence versus fall apart?

## 

To help keep submissions consistent and allow our parsing tools to aggregate community picks into **Post** #1, use this format when posting your setup:

```
[COMMUNITY-SUBMISSION]
tier: [≤16GB | 24-32GB | 48-64GB | 96-128GB | 196-256GB+]
hardware: [e.g., 8x RTX A4000 16GB, 2x RTX 3090, 1x RTX 4060 Ti 16GB, Mac Studio 64GB]
backend: [vLLM | llama.cpp | LM Studio | Ollama | Unsloth Desktop | Other]
model_name: [Full model repository or exact filename]
quant_package: [e.g., AutoRound W4A16, GGUF Q4_K_M, EXL2 4.0bpw, FP8]
download_url: [Hugging Face or model source link]
primary_use_case: [e.g., Coding & git refactoring, Deep reasoning, Creative writing, RAG]
stable_context_tested: [Actual token count tested without OOM or coherence collapse]
kv_cache_setting: [Native FP16/BF16 | FP8 | INT4]
throughput_tps: [Approximate generation tokens/sec]
setup_notes: [Launch flags, rope/yarn config, host RAM offload, prompt prefill behavior, quirks]
[/COMMUNITY-SUBMISSION]
```

*(You don’t have to fill out every line if you’re just dropping casual thoughts, but using the block makes it easy to integrate your numbers directly into the master matrix.)*

## 

This living guide is built for the forum. If there are things missing, unhelpful, or formatted poorly in **Post** #1, let me know:

- **What metrics should this thread track?** (e.g., Time-To-First-Token prefill latency, PCIe bandwidth bottlenecks, power draw/watts-per-token, speculative draft model pairings).
- **VRAM tier restructuring:** Do the current brackets (≤16GB, 24–32GB, 48–64GB, 96–128GB, 196–256GB+) reflect how you build systems, or should I adjust them (e.g., dedicated 24GB single-card tier, 48GB dual-card tier)?
- **Deployment examples:** Would container recipes (Docker Compose), systemd service templates, or specific vLLM / llama.cpp multi-GPU launch commands be helpful to include in collapsible details tabs?

Drop your configurations, battle-tested setups, and suggestions below!