Racka 4B — Hardware Requirements & GPU Compatibility
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Racka 4B is a 4.0B-parameter open language model from elte-nlp. At Q4_K_M it needs about 2.65 GB of VRAM — see which GPUs and Macs can run it below.
943 downloads
18 likesSpecifications
- Publisher
- elte-nlp
- Parameters
- 4.0B
- Release Date
- 2026-06-03
- License
- CC BY-NC-SA 4.0
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HuggingFace
How Much VRAM Does Racka 4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_S | 3.40 | 1.9 GB | — | 1.71 GB | Importance-weighted 3-bit, small |
| Q4_K_M | 4.80 | 2.6 GB | — | 2.41 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.1 GB | — | 2.87 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q8_0 | 8.00 | 4.4 GB | — | 4.02 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Racka 4B?
Q4_K_M · 2.6 GBRacka 4B (Q4_K_M) requires 2.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Racka 4B?
Q4_K_M · 2.6 GB33 devices with unified memory can run Racka 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Racka 4B need?
Racka 4B requires 2.6 GB of VRAM at Q4_K_M, or 4.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.6 GB- What's the best quantization for Racka 4B?
For Racka 4B, Q4_K_M (2.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.1 GB) provides better quality if you have the VRAM. The smallest option is IQ3_S at 1.9 GB.
VRAM requirement by quantization
IQ3_S1.9 GBQ4_K_M ★2.6 GBQ5_K_M3.1 GBQ8_04.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Racka 4B on a Mac?
Racka 4B requires at least 1.9 GB at IQ3_S, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Racka 4B locally?
Yes — Racka 4B can run locally on consumer hardware. At Q4_K_M quantization it needs 2.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Racka 4B?
At Q4_K_M, Racka 4B can reach ~1100 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~247 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 2.6 × 0.55 = ~1100 tok/s
Estimated speed at Q4_K_M (2.6 GB)
~1100 tok/s~247 tok/s~822 tok/s~680 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Racka 4B?
At Q4_K_M, the download is about 2.41 GB. The full-precision Q8_0 version is 4.02 GB. The smallest option (IQ3_S) is 1.71 GB.
- Which GPUs can run Racka 4B?
35 consumer GPUs can run Racka 4B at Q4_K_M (2.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Racka 4B?
33 devices with unified memory can run Racka 4B at Q4_K_M (2.6 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.