Pantheon Reasoning 26B A4B 1.1 — Hardware Requirements & GPU Compatibility
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Pantheon Reasoning 26B A4B 1.1 is a 26.5B-parameter open language model from Gryphe. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 16.57 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (IQ3_XS · 16K)Best on 24 GB (Q4_K_M · ~153 tok/s · 128K)Macs from 16 GB (Q2_K · ~45 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Pantheon Reasoning 26B A4B 1.1
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
ollama run hf.co/bartowski/Gryphe_Pantheon-Reasoning-26B-A4B-1.1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 7.1 GB free · 16K context
Download: Gryphe_Pantheon-Reasoning-26B-A4B-1.1-Q4_K_M.gguf17.45 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~145 tok/s
- generation speed
- Cost per 1M output tokens
- $0.35
- per 1M output tokens
How Much VRAM Does Pantheon Reasoning 26B A4B 1.1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 11.9 GB | 12.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~193 tok/s | |
| 12.3 GB | 13.2 GB | 3-bit small — 3-bit small quantization | Fits: 256K context · ~190 tok/s | |
| 13.6 GB | 14.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~177 tok/s | |
| 13.9 GB | 14.8 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 256K context · ~174 tok/s | |
| 16.6 GB | 17.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~153 tok/s | |
| 19.6 GB | 20.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~135 tok/s | |
| 22.5 GB | 23.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~120 tok/s | |
| 27.2 GB | 28.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 27.5 GB |
Selected Q4_K_M
4-bit medium quantization16.9GBat 16K context
- Weights
- 15.9 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 7.1 GB free · ~153 tok/s
≥50 tok/s needs ≥268 GB/s · RTX 5090 ~227, RTX 3090 Ti ~153
Which Hardware Can Run Pantheon Reasoning 26B A4B 1.1?
Q4_K_M · 16.6 GBPantheon Reasoning 26B A4B 1.1 (Q4_K_M) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 262K context window can add up to 8.6 GB, bringing total usage to 25.2 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Pantheon Reasoning 26B A4B 1.1, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
fits · 3.1 GB free · up to 64K ctx
1 GPU
RX 7900 XT~79Simulate output on RX 7900 XT32–40 GB
fits · 15.1 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~227Simulate output on RTX 5090M5 Max36 GB~63Simulate output on Mac Studio M5 Max (36 GB)M532 GB~28Simulate output on MacBook Air 13" M5 (32 GB)Orin~39Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~25Simulate 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 |
|---|---|---|---|---|
| Qwen3.8 27BAlibaba | 122.0 | 17.9 GB | ~38 | same size |
| Qwen3.6 27BAlibaba | 111.2 | 17.9 GB | ~38 | same size |
| Qwen3.5 27BAlibaba | 103.8 | 17.9 GB | ~38 | same size |
| Gemma 3 27B ITGoogle | 79.4 | 21.7 GB | ~36 | same size |
| Gemma 2 27B ITGoogle | 72.7 | 19.5 GB | ~37 | same size |
| Gemma 4 26B A4BGoogle | — | 16.5 GB | ~171 | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- Gryphe
- Parameters
- 26.5B
- Active parameters
- 5.1B per token
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-06-06
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Pantheon Reasoning 26B A4B 1.1 need?
Pantheon Reasoning 26B A4B 1.1 requires 16.6 GB of VRAM at Q4_K_M, or 53.7 GB at BF16. Full 262K context adds up to 8.6 GB (25.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 26.5B × 4.8 bits ÷ 8 = 15.9 GB
KV Cache + Overhead ≈ 0.7 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 ≈ 9.3 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Pantheon Reasoning 26B A4B 1.1?
Yes, at Q6_K (22.5 GB) or lower. Higher quantizations like Q8_0 (27.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Pantheon Reasoning 26B A4B 1.1?
For Pantheon Reasoning 26B A4B 1.1, Q4_K_M (16.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (16.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 8.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Pantheon Reasoning 26B A4B 1.1 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Pantheon Reasoning 26B A4B 1.1, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 16.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Pantheon Reasoning 26B A4B 1.1 locally?
Yes — Pantheon Reasoning 26B A4B 1.1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Pantheon Reasoning 26B A4B 1.1?
At Q4_K_M, Pantheon Reasoning 26B A4B 1.1 can reach ~150 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~153 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 3.2 GB active ÷ (8000 × 0.65) = 0.62 ms, plus 30 layers × 0.055 ms = 1.65 ms, so 1000 ÷ 2.27 ms = ~441 tok/s
Estimated speed at Q4_K_M (16.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Pantheon Reasoning 26B A4B 1.1?
At Q4_K_M, the download is about 15.93 GB. The full-precision BF16 version is 53.09 GB. The smallest option (IQ2_XS) is 7.96 GB.
Which GPUs can run Pantheon Reasoning 26B A4B 1.1?
8 consumer GPUs can run Pantheon Reasoning 26B A4B 1.1 at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Pantheon Reasoning 26B A4B 1.1?
122 devices with unified memory can run Pantheon Reasoning 26B A4B 1.1 at Q4_K_M (16.6 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.