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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~142 tok/s · 2K)Macs from 8 GB (Q4_K_S · ~17 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Pythia 6.9B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/pythia-6.9b-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 19.4 GB free · 2K context
Download: pythia-6.9b.Q4_K_M.gguf4.40 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~51 tok/s
- generation speed
- Cost per 1M output tokens
- $0.38
- per 1M output tokens
How Much VRAM Does Pythia 6.9B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~200 tok/s | |
| 3.4 GB | — | 3-bit small — 3-bit small quantization | Fits: 2K context · ~195 tok/s | |
| 3.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~175 tok/s | |
| 3.9 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 2K context · ~170 tok/s | |
| 4.6 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~142 tok/s | |
| 5.5 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~120 tok/s | |
| 6.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~103 tok/s | |
| 7.7 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~85 tok/s |
Selected Q4_K_M
4-bit medium quantization4.6GBat 2K context
- Weights
- 4.0 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.4 GB free · ~142 tok/s
≥50 tok/s needs ≥354 GB/s · RTX 5090 ~253, RTX 3090 Ti ~142
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Pythia 6.9B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.4 GB free · up to 2K ctx
16 GPUs · 2 device families
RTX 3080~107Simulate output on RTX 3080RTX 2080 Ti~87Simulate output on RTX 2080 TiRTX 3070 Ti~86Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 15.4 GB free · up to 2K ctx
1 GPU
RX 7900 XT~104Simulate output on RX 7900 XT32–40 GB
fits · 27.4 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~253Simulate output on RTX 5090M5 Max36 GB~70Simulate output on Mac Studio M5 Max (36 GB)M532 GB~23Simulate output on MacBook Air 13" M5 (32 GB)Orin~29Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~19Simulate 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 |
|---|---|---|---|---|
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Llava 1.5 7B HFllava-hf | — | 5 GB | ~141 | same size |
| Baichuan2 7B Basebaichuan-inc | — | 5 GB | ~142 | same size |
Specifications
- 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
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)
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 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Pythia 6.9B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Pythia 6.9B, the smallest at Q4_K_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Pythia 6.9B locally?
Yes — Pythia 6.9B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Pythia 6.9B?
At Q4_K_M, Pythia 6.9B can reach ~1041 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 ÷ 4.61 × 0.65 = ~1128 tok/s
Estimated speed at Q4_K_M (4.6 GB)
Real-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?
52 consumer GPUs can run Pythia 6.9B 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 Pythia 6.9B?
151 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, 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.