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Llama 3.2 11B Instruct (Inference net)
◆ OPEN
by Meta
OPEN WEIGHTSLOCALAPICLOSED
RANKED #312 OF 340 ASSISTANTS · OVERALL #488 OF 7,140 · VIBE SCORE 5.0 · 25 VOICES
Open Llama instruction model for multilingual chat, reasoning, and coding
HAPPY5.9
1 mentions
SAD9.1
3 mentions
VIBE TRAJECTORY
26 weeks · 26 voices
HOLDING UP
Recent vibe is steady. No clear fade in the crowd.
HOW TO USE IT
Open weights. You can use a hosted service, or download it and run it yourself, free.
RUN IT ON YOUR OWN MACHINE
Open weights: download Llama 3.2 11B Instruct (Inference net) once and it's yours. No subscription, no rate limits, works offline.
11B parameters, a Q4_K_M quant is about 6.7 GB while unquantized 16-bit is ~22 GB
- BARE MINIMUM
NVIDIA GeForce RTX 3060 12GB
12 GB VRAM
~$250 USEDQ4_K_M quant, ~38 tok/s, 8k context
- SWEET SPOT
NVIDIA GeForce RTX 4070 12GB
12 GB GDDR6X
~$520 NEWQ4_K_M to Q5_K_M quant, ~60 tok/s, 16k context
- FULL POWER
NVIDIA GeForce RTX 4090 24GB
24 GB VRAM
~$1750 USEDBF16 / FP16 unquantized or Q8, ~85 tok/s, full 128k context
LEARN HOW TO SET IT UP
WILL MY BOX RUN LLAMA 3.2 11B INSTRUCT (INFERENCE NET)?
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
WHAT CAN YOU ACTUALLY DO WITH IT?
Everyday jobs on each of those rigs: how long Llama 3.2 11B Instruct (Inference net) takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNVIDIA GeForce RTX 3060 12GB | SWEET SPOTNVIDIA GeForce RTX 4070 12GB | FULL POWERNVIDIA GeForce RTX 4090 24GB |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~13 S | ~8 S | ~6 S |
| Summarize a documenta long report boiled down to the points that matter | ~39 S★★★★★ | ~25 S★★★★★ | ~18 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~3.5 MIN★★★★★ | ~2.2 MIN★★★★★ | ~1.6 MIN★★★★★ |
| Build a backendan API with routes, storage and tests | ~11 MIN★★★★★ | ~6.9 MIN★★★★★ | ~4.9 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~6.6 MIN★★★★★ | ~4.2 MIN★★★★★ | ~2.9 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
WHAT THE INTERNET SAYS
Not enough discussion about Llama 3.2 11B Instruct (Inference net) yet to call it. We found 25 posts but people did not say much either way.
25 POSTS · 0 COMMENTS · REDDIT · GITHUB
PEOPLE LOVE
- No fan mail yet. The crowd is still warming up.
PEOPLE COMPLAIN ABOUT
- No complaints yet. The internet is still looking for one. Suspicious.
LOOKING FOR AN ALTERNATIVE?
Other models the crowd has fully reviewed, starting with multimodal models like this one.
Grok 4.6
Qwen3.8 27B> SOURCES (12)
- REDDIT · 2026-01-21 · 8 UPVOTESTrain Llama-3.2-11b-Vision-Instruct with GRPO
Train Llama-3.2-11b-Vision-Instruct with GRPO
- REDDIT · 2025-05-07 · 8 UPVOTESI wrote a basic multimodal (Image and Text) agentic layer for my custom finetuned model
I wrote a basic multimodal (Image and Text) agentic layer for my custom finetuned model
- GITHUB · 2026-08-23 · 2 UPVOTESfix(ai): replace deprecated default model with Llama 4 Scout 17B (Hindi-supported)
fix(ai): replace deprecated default model with Llama 4 Scout 17B (Hindi-supported)
- REDDIT · 2025-10-05 · 2 UPVOTESTraining a Vision model on a Text-Only Dataset using Axolotl
Training a Vision model on a Text-Only Dataset using Axolotl
- REDDIT · 2026-04-18 · 0 UPVOTESWe caught cloud providers silently hot-swapping LLMs (Bait-and-Switch) using a cryptographic memory DAG.
We caught cloud providers silently hot-swapping LLMs (Bait-and-Switch) using a cryptographic memory DAG.
- GITHUB · 2026-09-05 · 0 UPVOTESAdd setup and troubleshooting documentation for Llama 3.2 Vision with vLLM
Add setup and troubleshooting documentation for Llama 3.2 Vision with vLLM
- GITHUB · 2026-09-14 · 0 UPVOTES[BUG][P1] CIO LLM response fails schema validation on llama-3.2-11b-vision-instruct — LLM_PARSE_FAILURE_SKIP every affected loop
[BUG][P1] CIO LLM response fails schema validation on llama-3.2-11b-vision-instruct — LLM_PARSE_FAILURE_SKIP every affected loop
- GITHUB · 2026-08-24 · 0 UPVOTESLatest: Llama-Mobile: Efficient 2.7-Bit Quantization of VLMs
Latest: Llama-Mobile: Efficient 2.7-Bit Quantization of VLMs
- GITHUB · 2026-09-01 · 0 UPVOTESUpdate NIM model to 'meta/llama-3.2-11b-vision-instruct' across confi…
Update NIM model to 'meta/llama-3.2-11b-vision-instruct' across confi…
- GITHUB · 2026-09-14 · 0 UPVOTESAdd multimodal vision support to /ask endpoint using Llama 3.2 Vision
Add multimodal vision support to /ask endpoint using Llama 3.2 Vision
- GITHUB · 2026-09-18 · 0 UPVOTESfix: upgrade NVIDIA fallback to valid LLaMA 3.2 model
fix: upgrade NVIDIA fallback to valid LLaMA 3.2 model
- GITHUB · 2026-09-18 · 0 UPVOTESSerialize NVIDIA NIM tool-calls for llama-3.2-11b-vision-instruct
Serialize NVIDIA NIM tool-calls for llama-3.2-11b-vision-instruct