Manaca 1B Instruct — Hardware Requirements & GPU Compatibility
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Manaca 1B Instruct is a 1.7B-parameter open language model from menezesbruno. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 1.43 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 · 4K)Best on 24 GB (Q4_K_M · ~458 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~50 tok/s)
CC BY-NC 4.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Manaca 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
vllm serve menezesbruno/manaca-1b-instruct --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~415 tok/s
- generation speed
- Cost per 1M output tokens
- $0.082
- per 1M output tokens
How Much VRAM Does Manaca 1B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.1 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~580 tok/s | |
| 1.2 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~528 tok/s | |
| 1.4 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~458 tok/s | |
| 1.6 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~402 tok/s | |
| 1.8 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~360 tok/s | |
| 2.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~309 tok/s | |
| 3.9 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~170 tok/s |
Selected Q4_K_M
4-bit medium quantization1.5GBat 4K context
- Weights
- 1.0 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.5 GB free · ~458 tok/s
≥50 tok/s needs ≥110 GB/s · RTX 5090 ~815, RTX 3090 Ti ~458
Which Hardware Can Run Manaca 1B Instruct?
Q4_K_M · 1.4 GBManaca 1B Instruct (Q4_K_M) requires 1.4 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 4K context window can add up to 0.1 GB, bringing total usage to 1.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Manaca 1B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.5 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~346Simulate output on RTX 3080RTX 2080 Ti~280Simulate output on RTX 2080 TiRTX 3070 Ti~277Simulate output on RTX 3070 TiM3*8 GB~50Simulate output on iMac 24" M3 (8 GB)Orin Nano~46Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~33Simulate output on iPhone 1720 GB
fits · 18.5 GB free · up to 4K ctx
1 GPU
RX 7900 XT~336Simulate output on RX 7900 XT32–40 GB
fits · 30.5 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~815Simulate output on RTX 5090M5 Max36 GB~225Simulate output on Mac Studio M5 Max (36 GB)M532 GB~75Simulate output on MacBook Air 13" M5 (32 GB)Orin~93Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~61Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM3 1.7BLiquid AI | — | 2.1 GB | ~455 | same size |
| Granite 4.0 1BIBM | — | 2.6 GB | ~452 | same size |
| Qwen 1 8BAlibaba | — | 1.9 GB | ~542 | same size |
| Qwen3 1.7B BaseAlibaba | — | 3.2 GB | ~417 | same size |
| DeepSeek R1 Distill Qwen 1.5BDeepSeek | — | 1.8 GB | ~461 | same size |
| SmolLM2 1.7B InstructHugging Face | — | 2.9 GB | ~379 | same size |
Specifications
- Publisher
- menezesbruno
- Parameters
- 1.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 64,000
- Release Date
- 2026-09-03
- License
- CC BY-NC 4.0
Frequently Asked Questions
How much VRAM does Manaca 1B Instruct need?
Manaca 1B Instruct requires 1.4 GB of VRAM at Q4_K_M, or 3.9 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 4.8 bits ÷ 8 = 1 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 ≈ 0.5 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Manaca 1B Instruct?
For Manaca 1B Instruct, Q4_K_M (1.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Manaca 1B Instruct on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Manaca 1B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Manaca 1B Instruct locally?
Yes — Manaca 1B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Manaca 1B Instruct?
At Q4_K_M, Manaca 1B Instruct can reach ~3357 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~458 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 ÷ 1.43 × 0.65 = ~3636 tok/s
Estimated speed at Q4_K_M (1.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Manaca 1B Instruct?
At Q4_K_M, the download is about 1.03 GB. The full-precision BF16 version is 3.44 GB. The smallest option (Q2_K) is 0.73 GB.
Which GPUs can run Manaca 1B Instruct?
52 consumer GPUs can run Manaca 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 Manaca 1B Instruct?
163 devices with unified memory can run Manaca 1B Instruct at Q4_K_M (1.4 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.