Pantheon Reasoning 27B — Hardware Requirements & GPU Compatibility
ChatRoleplayReasoning
Pantheon Reasoning 27B is a 27.8B-parameter open language model from Gryphe. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 17.42 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 12 GB (IQ3_XXS · 16K)Best on 24 GB (Q4_K_M · ~38 tok/s · 64K)Macs from 16 GB (IQ3_XXS · ~12 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Pantheon Reasoning 27B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/bartowski/Gryphe_Pantheon-Reasoning-27B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 6.1 GB free · 16K context
Download: Gryphe_Pantheon-Reasoning-27B-Q4_K_M.gguf17.98 GBbartowski
Other ways
each opens step-by-stepRun in cloud
Fits on RTX A5000 (24 GB) (6 GB headroom) · Q4_K_M
- Generation speed
- ~29 tok/s
- generation speed
- Cost per 1M output tokens
- $1.55
- per 1M output tokens
How Much VRAM Does Pantheon Reasoning 27B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 12.6 GB | 13.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~52 tok/s | |
| 12.9 GB | 14.2 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~51 tok/s | |
| 14.3 GB | 15.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~46 tok/s | |
| 14.6 GB | 16 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~45 tok/s | |
| 17.4 GB | 18.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~38 tok/s | |
| 20.5 GB | 21.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~32 tok/s | |
| 23.7 GB | 25 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~28 tok/s | |
| 28.5 GB | 29.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 29 GB |
Selected Q4_K_M
4-bit medium quantization17.9GBat 16K context
- Weights
- 16.7 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 6.1 GB free · ~38 tok/s
≥50 tok/s needs ≥1.34 TB/s · RTX 5090 ~67
Which Hardware Can Run Pantheon Reasoning 27B?
Q4_K_M · 17.4 GBPantheon Reasoning 27B (Q4_K_M) requires 17.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 262K context window can add up to 13.9 GB, bringing total usage to 31.3 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 27B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
fits · 2.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~28Simulate output on RX 7900 XT32–40 GB
fits · 14.1 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~67Simulate output on RTX 5090M5 Max36 GB~19Simulate output on Mac Studio M5 Max (36 GB)M532 GB~6Simulate output on MacBook Air 13" M5 (32 GB)Orin~8Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~5Simulate 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 | 122.1 | 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 |
| Muse Glimmer 30BMeta | — | 19.6 GB | ~36 | same size |
| Gemma 4 26B A4BGoogle | — | 16.9 GB | ~153 | same size |
| ERNIE 4.5 VL 28B A3B PTBaidu | — | 60 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- Gryphe
- Parameters
- 27.8B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-05-29
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Pantheon Reasoning 27B need?
Pantheon Reasoning 27B requires 17.4 GB of VRAM at Q4_K_M, or 56.3 GB at BF16. Full 262K context adds up to 13.9 GB (31.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27.8B × 4.8 bits ÷ 8 = 16.7 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 ≈ 14.6 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Pantheon Reasoning 27B?
Yes, at Q6_K (23.7 GB) or lower. Higher quantizations like Q8_0 (28.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Pantheon Reasoning 27B?
For Pantheon Reasoning 27B, Q4_K_M (17.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (17.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Pantheon Reasoning 27B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Pantheon Reasoning 27B, the smallest at IQ3_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 17.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Pantheon Reasoning 27B locally?
Yes — Pantheon Reasoning 27B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Pantheon Reasoning 27B?
At Q4_K_M, Pantheon Reasoning 27B can reach ~276 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~38 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 ÷ 17.4 × 0.65 = ~299 tok/s
Estimated speed at Q4_K_M (17.4 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 27B?
At Q4_K_M, the download is about 16.67 GB. The full-precision BF16 version is 55.56 GB. The smallest option (IQ2_XXS) is 7.64 GB.
Which GPUs can run Pantheon Reasoning 27B?
8 consumer GPUs can run Pantheon Reasoning 27B 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 27B?
122 devices with unified memory can run Pantheon Reasoning 27B at Q4_K_M (17.4 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.