Meme Trix MoE 14B A8B V1 — Hardware Requirements & GPU Compatibility
Chat
Meme Trix MoE 14B A8B V1 is a 13.7B-parameter open language model from Naphula. It supports a context window of up to 1,073,152 tokens. At Q4_K_M it needs about 8.77 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~104 tok/s · 64K)Macs from 16 GB (Q4_K_M · ~23 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Meme Trix MoE 14B A8B V1
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Naphula/Meme-Trix-MoE-14B-A8B-v1 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX A4000 (16 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~52 tok/s
- generation speed
- Cost per 1M output tokens
- $0.59
- per 1M output tokens
How Much VRAM Does Meme Trix MoE 14B A8B V1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.4 GB | 10.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~134 tok/s | |
| 7.2 GB | 11.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~122 tok/s | |
| 8.8 GB | 12.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~104 tok/s | |
| 10.3 GB | 14.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~91 tok/s | |
| 11.8 GB | 15.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~81 tok/s | |
| 14.2 GB | 18.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~69 tok/s | |
| 27.9 GB | 32 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 29.8 GB |
Selected Q4_K_M
4-bit medium quantization10.7GBat 16K context
- Weights
- 8.2 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 13.4 GB free · ~104 tok/s
≥50 tok/s needs ≥432 GB/s · RTX 5090 ~162, RTX 3090 Ti ~104
Which Hardware Can Run Meme Trix MoE 14B A8B V1?
Q4_K_M · 8.8 GBMeme Trix MoE 14B A8B V1 (Q4_K_M) requires 8.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 1073K context window can add up to 140.4 GB, bringing total usage to 149.2 GB. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Meme Trix MoE 14B A8B V1, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 1.4 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~96Simulate output on RTX 3080 TiRTX 5070~74Simulate output on RTX 5070RTX 4070~57Simulate output on RTX 407020 GB
fits · 9.3 GB free · up to 64K ctx
1 GPU
RX 7900 XT~59Simulate output on RX 7900 XT32–40 GB
fits · 21.4 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~162Simulate output on RTX 5090M5 Max36 GB~45Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate 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 |
|---|---|---|---|---|
| Phi 4 ReasoningMicrosoft | — | 12.5 GB | ~69 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Qwen1.5 14BAlibaba | — | 22.2 GB | ~63 | same size |
| Baichuan2 13B Basebaichuan-inc | — | 11.5 GB | ~76 | same size |
| Llama 2 13B Chat HFMeta | — | 11.5 GB | ~76 | same size |
| Vicuna 13B V1.3LMSYS | — | 8.6 GB | ~76 | same size |
Specifications
- Publisher
- Naphula
- Parameters
- 13.7B
- Active parameters
- 8B per token
- Architecture
- MixtralForCausalLM
- Context Length
- 1,073,152 tokens
- Vocabulary Size
- 129,024
- Release Date
- 2026-02-21
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Meme Trix MoE 14B A8B V1 need?
Meme Trix MoE 14B A8B V1 requires 8.8 GB of VRAM at Q4_K_M, or 27.9 GB at BF16. Full 1073K context adds up to 140.4 GB (149.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 13.7B × 4.8 bits ÷ 8 = 8.2 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 141 GB (at full 1073K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Meme Trix MoE 14B A8B V1?
Yes, at Q8_0 (14.2 GB) or lower. Higher quantizations like BF16 (27.9 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Meme Trix MoE 14B A8B V1?
For Meme Trix MoE 14B A8B V1, Q4_K_M (8.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Meme Trix MoE 14B A8B V1 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Meme Trix MoE 14B A8B V1. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 8.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Meme Trix MoE 14B A8B V1 locally?
Yes — Meme Trix MoE 14B A8B V1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Meme Trix MoE 14B A8B V1?
At Q4_K_M, Meme Trix MoE 14B A8B V1 can reach ~134 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~104 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 → 5.1 GB active ÷ (8000 × 0.65) = 0.99 ms, plus 32 layers × 0.055 ms = 1.76 ms, so 1000 ÷ 2.75 ms = ~364 tok/s
Estimated speed at Q4_K_M (8.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Meme Trix MoE 14B A8B V1?
At Q4_K_M, the download is about 8.20 GB. The full-precision BF16 version is 27.35 GB. The smallest option (Q2_K) is 5.81 GB.
Which GPUs can run Meme Trix MoE 14B A8B V1?
40 consumer GPUs can run Meme Trix MoE 14B A8B V1 at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Meme Trix MoE 14B A8B V1?
150 devices with unified memory can run Meme Trix MoE 14B A8B V1 at Q4_K_M (8.8 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.