Llama 3.2 1B Instruct — Hardware Requirements & GPU Compatibility
ChatFunctions
Meta Llama 3.2 1B Instruct is a 1-billion parameter instruction-tuned model from Meta, the smallest in the Llama 3.2 family. It is designed for ultra-lightweight deployment scenarios where minimal hardware resources are available, supporting a 128K token context window despite its compact size. This model is suitable for basic conversational tasks, text summarization, and simple instruction following. It can run on virtually any modern GPU and even on CPU-only setups with acceptable performance. Released under the Llama 3.2 Community License.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 64K)Best on 24 GB (Q4_K_M · ~799 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~87 tok/s)
llama3.2
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3.2 1B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run llama3.2:1b-instruct-q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.7 GB free · 16K context
Download: Llama-3.2-1B-Instruct-Q4_K_M.gguf0.81 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~285 tok/s
- generation speed
- Cost per 1M output tokens
- $0.058
- per 1M output tokens
How Much VRAM Does Llama 3.2 1B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.6 GB | 3.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~1130 tok/s | |
| 0.6 GB | 3.9 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~1111 tok/s | |
| 0.7 GB | 3.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~993 tok/s | |
| 0.7 GB | 3.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~964 tok/s | |
| 0.8 GB | 4.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~799 tok/s | |
| 1.0 GB | 4.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~676 tok/s | |
| 1.1 GB | 4.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~585 tok/s | |
| 1.4 GB | 4.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~482 tok/s |
Selected Q4_K_M
4-bit medium quantization2.3GBat 16K context
- Weights
- 0.3 GB
- KV cache
- 1.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.7 GB free · ~799 tok/s
≥50 tok/s needs ≥63 GB/s · RTX 5090 ~1421, RTX 3090 Ti ~799
Which Hardware Can Run Llama 3.2 1B Instruct?
Q4_K_M · 0.8 GBLlama 3.2 1B Instruct (Q4_K_M) requires 0.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Llama 3.2 1B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.7 GB free · up to 64K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~603Simulate output on RTX 3080RTX 2080 Ti~488Simulate output on RTX 2080 TiRTX 3070 Ti~482Simulate output on RTX 3070 TiM3*8 GB~87Simulate output on iMac 24" M3 (8 GB)Orin Nano~81Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~58Simulate output on iPhone 1720 GB
fits · 17.7 GB free · up to 128K ctx
1 GPU
RX 7900 XT~585Simulate output on RX 7900 XT32–40 GB
fits · 29.7 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1421Simulate output on RTX 5090M5 Max36 GB~393Simulate output on Mac Studio M5 Max (36 GB)M532 GB~131Simulate output on MacBook Air 13" M5 (32 GB)Orin~162Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~107Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Llama 3.2 1B Instruct?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 4 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GPQA Diamond
Epoch AI · Sep 2024Independent
Independent23.9%
#94/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1111.1
#182/188 open
- AIME 2024/2025
Epoch AI · Sep 2024Independent
Independent0.6%
#85/88 open
- BALROG
Epoch AI · Sep 2024Independent
Independent6.6%
#14/15 open
Alternatives to Llama 3.2 1B Instruct
Llama 3 family · 65 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MinerU2.5 Pro 2605 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| MiniCPM V 4.6 ThinkingOpenBMB | — | 1.5 GB | ~580 | same size |
| MinerU2.5 Pro 2604 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| HRM Text 1Bsapientinc | — | 3.1 GB | ~228 | same size |
| LFM2.5 1.2B ThinkingLiquid AI | — | 1.2 GB | ~612 | same size |
| Ilama 3.2 1Bhmellor | — | 3.3 GB | ~231 | same size |
Fine-tunes & variants(3)
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 1.2B
- Context Length
- 131,072 tokens
- Release Date
- 2024-09-18
- License
- llama3.2
Frequently Asked Questions
How much VRAM does Llama 3.2 1B Instruct need?
Llama 3.2 1B Instruct requires 0.8 GB of VRAM at Q4_K_M, or 2.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.2B × 4.8 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Llama 3.2 1B Instruct?
For Llama 3.2 1B Instruct, Q4_K_M (0.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (0.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.2 1B Instruct on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Llama 3.2 1B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 3.2 1B Instruct locally?
Yes — Llama 3.2 1B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.2 1B Instruct?
At Q4_K_M, Llama 3.2 1B Instruct can reach ~5854 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~799 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 0.8200 × 0.65 = ~6341 tok/s
Estimated speed at Q4_K_M (0.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 3.2 1B Instruct?
At Q4_K_M, the download is about 0.74 GB. The full-precision BF16 version is 2.47 GB. The smallest option (IQ2_XXS) is 0.34 GB.
Which GPUs can run Llama 3.2 1B Instruct?
52 consumer GPUs can run Llama 3.2 1B Instruct at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Llama 3.2 1B Instruct?
163 devices with unified memory can run Llama 3.2 1B Instruct at Q4_K_M (0.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.