Llama 3.2 Mavrick.Spark 1B — Hardware Requirements & GPU Compatibility
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Llama 3.2 Mavrick.Spark 1B is a 1.2B-parameter open language model from 11-47 in the Llama 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 1.11 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~590 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~65 tok/s)
llama3.2
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3.2 Mavrick.Spark 1B
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
vllm serve 11-47/Llama-3.2-Mavrick.Spark-1B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (10 GB headroom) · Q4_K_M
- Generation speed
- ~211 tok/s
- generation speed
- Cost per 1M output tokens
- $0.078
- per 1M output tokens
How Much VRAM Does Llama 3.2 Mavrick.Spark 1B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.9 GB | 1.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~736 tok/s | |
| 1.0 GB | 2 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~676 tok/s | |
| 1.1 GB | 2.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~590 tok/s | |
| 1.3 GB | 2.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~524 tok/s | |
| 1.4 GB | 2.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~471 tok/s | |
| 1.6 GB | 2.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~410 tok/s | |
| 2.8 GB | 3.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~231 tok/s |
Selected Q4_K_M
4-bit medium quantization1.6GBat 16K context
- Weights
- 0.7 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.4 GB free · ~590 tok/s
≥50 tok/s needs ≥85 GB/s · RTX 5090 ~1049, RTX 3090 Ti ~590
Which Hardware Can Run Llama 3.2 Mavrick.Spark 1B?
Q4_K_M · 1.1 GBLlama 3.2 Mavrick.Spark 1B (Q4_K_M) requires 1.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 131K context window can add up to 4.2 GB, bringing total usage to 5.3 GB. 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 Mavrick.Spark 1B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.4 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~445Simulate output on RTX 3080RTX 2080 Ti~361Simulate output on RTX 2080 TiRTX 3070 Ti~356Simulate output on RTX 3070 TiM3*8 GB~65Simulate output on iMac 24" M3 (8 GB)Orin Nano~60Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~43Simulate output on iPhone 1720 GB
fits · 18.4 GB free · up to 128K ctx
1 GPU
RX 7900 XT~432Simulate output on RX 7900 XT32–40 GB
fits · 30.4 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1049Simulate output on RTX 5090M5 Max36 GB~290Simulate output on Mac Studio M5 Max (36 GB)M532 GB~97Simulate output on MacBook Air 13" M5 (32 GB)Orin~120Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~79Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Llama 3.2 Mavrick.Spark 1B
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 |
Specifications
- Publisher
- 11-47
- Family
- Llama 3
- Parameters
- 1.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-07-15
- License
- llama3.2
Frequently Asked Questions
How much VRAM does Llama 3.2 Mavrick.Spark 1B need?
Llama 3.2 Mavrick.Spark 1B requires 1.1 GB of VRAM at Q4_K_M, or 2.8 GB at BF16. Full 131K context adds up to 4.2 GB (5.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.2B × 4.8 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.4 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.
KV Cache + Overhead ≈ 4.6 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Llama 3.2 Mavrick.Spark 1B?
For Llama 3.2 Mavrick.Spark 1B, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.2 Mavrick.Spark 1B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Llama 3.2 Mavrick.Spark 1B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 3.2 Mavrick.Spark 1B locally?
Yes — Llama 3.2 Mavrick.Spark 1B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.2 Mavrick.Spark 1B?
At Q4_K_M, Llama 3.2 Mavrick.Spark 1B can reach ~4324 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~590 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 ÷ 1.11 × 0.65 = ~4685 tok/s
Estimated speed at Q4_K_M (1.1 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 Mavrick.Spark 1B?
At Q4_K_M, the download is about 0.74 GB. The full-precision BF16 version is 2.47 GB. The smallest option (Q2_K) is 0.53 GB.
Which GPUs can run Llama 3.2 Mavrick.Spark 1B?
52 consumer GPUs can run Llama 3.2 Mavrick.Spark 1B 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 Mavrick.Spark 1B?
163 devices with unified memory can run Llama 3.2 Mavrick.Spark 1B at Q4_K_M (1.1 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.