Pythia 6.9B — Hardware Requirements & GPU Compatibility
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Pythia 6.9B is a 7.0B-parameter open language model from EleutherAI. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 4.61 GB of VRAM — see which GPUs and Macs can run it below.
217.2K downloads
64 likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 7.0B
- Architecture
- GPTNeoXForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,432
- Release Date
- 2023-02-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Pythia 6.9B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.3 GB | — | 2.97 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.4 GB | — | 3.06 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.8 GB | — | 3.41 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.9 GB | — | 3.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.6 GB | — | 4.19 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.5 GB | — | 4.98 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.3 GB | — | 5.77 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.7 GB | — | 6.99 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Pythia 6.9B?
Q4_K_M · 4.6 GBPythia 6.9B (Q4_K_M) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Pythia 6.9B?
Q4_K_M · 4.6 GB59 devices with unified memory can run Pythia 6.9B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Pythia 6.9B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does Pythia 6.9B need?
Pythia 6.9B requires 4.6 GB of VRAM at Q4_K_M, or 15.4 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 7.0B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.6 GB- What's the best quantization for Pythia 6.9B?
For Pythia 6.9B, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.1 GB.
VRAM requirement by quantization
IQ2_XXS2.1 GBIQ3_XS3.2 GBQ3_K_M3.8 GBQ4_K_M ★4.6 GBQ5_K_S5.3 GBFP1615.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Pythia 6.9B on a Mac?
Pythia 6.9B requires at least 2.1 GB at IQ2_XXS, 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 Pythia 6.9B locally?
Yes — Pythia 6.9B can run locally on consumer hardware. At Q4_K_M quantization it needs 4.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Pythia 6.9B?
At Q4_K_M, Pythia 6.9B can reach ~954 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~142 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 ÷ 4.6 × 0.65 = ~1128 tok/s
Estimated speed at Q4_K_M (4.6 GB)
~1128 tok/s~142 tok/s~1128 tok/s~954 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Pythia 6.9B?
At Q4_K_M, the download is about 4.19 GB. The full-precision FP16 version is 13.98 GB. The smallest option (IQ2_XXS) is 1.92 GB.
- Which GPUs can run Pythia 6.9B?
50 consumer GPUs can run Pythia 6.9B at Q4_K_M (4.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Pythia 6.9B?
59 devices with unified memory can run Pythia 6.9B at Q4_K_M (4.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.