Llama 3.1 34B Hardware Requirements: What GPU Do You Actually Need?

Wait — Does Llama 3.1 Even Have a 34B Model?

No. And that's the first thing to clarify because half the searches for "Llama 3.1 34B" land on this page instead of getting a straight answer.

Meta's Llama 3.1 lineup is 8B, 70B, and 405B. That's it. The 34B parameter models floating around come from Llama 2 34B (older, from 2023) or CodeLlama 34B (also from Llama 2 era, optimized for code). Both work fine for local inference. Both are mature. But neither is Llama 3.1.

This guide covers hardware requirements for running any 34B parameter model — Llama 2 34B, CodeLlama 34B, or any other quantized variant you find on HuggingFace. If you specifically want the newest Meta model, jump to the Llama 3.1 70B guide instead. But if you're here because you want a capable open-weight model that fits on a single consumer GPU without dropping $1,500+, keep reading.

TL;DR: Here's What You Need

CodeLlama 34B Q4_K_M requires 20GB VRAM. Buy an RTX 3090 (24GB, used ~$950–$1,100) or RTX 4070 Ti Super 16GB (new ~$1,179, can run Q3_K_M fully or Q4_K_M with light CPU offload). Skip the RTX 4070 12GB — it's not enough. A 16GB card is the minimum for comfortable 34B inference.

Best for: builders who want a smart, capable model (better coding than 8B, faster than 70B) without overhauling their entire system.

The VRAM Breakdown by Quantization

A 34B model's VRAM footprint depends entirely on its quantization. Here's what you actually need:

Notes

Significant quality loss. Not recommended.

Good quality. Works on RTX 4070 Ti Super with 16GB. Expect 12–16 tok/s.

Faster computation than Q4_K_M but slightly lower quality. Practical if you need speed.

The standard "best balance." Excellent quality. ~18–22 tok/s on modern cards.

Exceeds consumer GPU VRAM. Not practical for home builds.

Enterprise only. Not worth considering. The real world: CodeLlama 34B Q4_K_M on HuggingFace is 20.2GB. Add ~2–5GB for KV cache (context window overhead) and you're looking at 22–25GB under typical use.

Which GPU Should You Buy?

This depends on your budget and whether you already own GPU hardware.

RTX 3090 (24GB) — Best for 34B, If You Can Find One

• Runs: CodeLlama 34B Q4_K_M at full speed, no offloading
• Performance: ~18–22 tok/s (steady, reliable inference)
• Current price: Used $950–$1,100. New ones don't exist anymore.
• Power draw: 420W. Loud. Runs hot.
• Verdict: Still the easiest single card for 34B. If you find a used one under $1,050, it's a solid deal. Just accept the noise and power bill.

RTX 4070 Ti Super 16GB — The New Sweet Spot

• Runs: CodeLlama 34B Q3_K_M fully in VRAM, Q4_K_M with ~30% CPU offload
• Performance: Q3_K_M gets 14–18 tok/s. Q4_K_M with offload drops to 8–12 tok/s (slower, but workable)
• Current price: New $1,179. Used $750–$850.
• Power draw: 285W. Quiet. Efficient.
• Verdict: The smarter buy in 2026. Newer architecture, runs cold, and gives you room for future models (Llama 3.1 70B at Q3_K_M fits here too, just barely). If you're not in a rush to max out inference speed, this is the pick.

RTX 4060 Ti 16GB — Budget Compromise

• Runs: CodeLlama 34B Q3_K_M comfortably, Q4_K_M with moderate offload
• Performance: Q3_K_M around 12–15 tok/s. Q4_K_M with offload 6–10 tok/s.
• Current price: Used $500–$650.
• Power draw: 210W.
• Verdict: Good if you're stretching your budget. Slower than the Ti Super, but 50% less cost. Underrated option if you can tolerate a few extra seconds per response.

RTX 4070 (12GB) — Don't.

The base 4070 has 12GB VRAM, which is short by 8GB for a 34B Q4 model. You'll spend half your compute time pushing layers to CPU, and inference speed becomes single-digit tok/s. For 34B, step up to the 4070 Ti Super or find a used 3090. The $200 gap closes fast once you factor in the frustration.

RX 7900 XTX (24GB) — If You're AMD-All-In

• Same VRAM as RTX 3090.
• Typically ~10–15% slower token generation (AMD's LLM optimizations lag NVIDIA's).
• ~$150–$300 cheaper than a used RTX 3090.
• Verdict: Solid alternative if AMD is your ecosystem. Not faster, but it's there if you prefer it.

CPU Offloading: The Fallback When GPU VRAM Isn't Enough

If you're stuck with a 12GB card but want to run 34B anyway, CPU offloading lets you move some layers to system RAM. It's slow, but it works.

How it works: llama.cpp has a --n-gpu-layers flag (or -ngl for short). Set it lower than the total layers (CodeLlama 34B has 49 layers) and the rest go to RAM.

Example: --n-gpu-layers 25 offloads 25 layers to GPU, leaves 24 in RAM.

Performance impact: Each layer in RAM costs massive speed. Going from 40 GPU layers to 25 GPU layers on an RTX 4070 12GB drops from ~8 tok/s to ~4–5 tok/s. It's painful, but if you're running once per day for code generation, it's livable.

System RAM needed: If you offload 24 layers to CPU, allocate ~30GB of system RAM. DDR5 is noticeably faster than DDR4 for this (roughly 20–25% better throughput on bandwidth-heavy layer operations). Grab 32GB DDR5–6000 or 64GB DDR4–3200 and you're set.

Pure CPU inference (no GPU): Don't. CodeLlama 34B on a Ryzen 9 9950X (top-tier CPU) still only manages 1–3 tok/s. That's unusable for real work.

Practical Commands to Get Started

Download the Model

huggingface-cli download TheBloke/CodeLlama-34B-Instruct-GGUF \
  codellama-34b-instruct.Q4_K_M.gguf --local-dir .

or for faster downloads on gigabit connections:

HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download \
  TheBloke/CodeLlama-34B-Instruct-GGUF \
  codellama-34b-instruct.Q4_K_M.gguf \
  --local-dir . --local-dir-use-symlinks False

Run with RTX 3090 (Full GPU, No Offloading)

llama-cli -m codellama-34b-instruct.Q4_K_M.gguf \
  -ngl 49 \
  -n 512 \
  -p "def fibonacci(n):"

(The -ngl 49 offloads all 49 layers to GPU. Change to lower numbers like 25 or 35 if offloading to CPU.)

Run with RTX 4070 Ti Super (Partial Offload)

llama-cli -m codellama-34b-instruct.Q4_K_M.gguf \
  -ngl 32 \
  -n 512 \
  -p "def fibonacci(n):"

(32 layers on GPU, ~17 in RAM. Adjust -ngl up or down based on available VRAM.)

Run with RTX 4060 Ti 16GB (Q3_K_M Recommended)

llama-cli -m codellama-34b-instruct.Q3_K_M.gguf \
  -ngl 49 \
  -n 512 \
  -p "def fibonacci(n):"

(Q3_K_M at 14GB fits comfortably. If you insist on Q4_K_M, drop -ngl to 30–35.)

Should You Wait for Llama 3.1 Mid-Range?

There's a gap right now: Llama 3.1 8B (too small for serious work) and Llama 3.1 70B (overkill for many use cases, needs 24+ GB). The 34B fills that gap perfectly, but it's an older model.

Will Meta release a Llama 3.1 34B? Unknown. Industry rumors suggest they're focusing on 8B and 70B optimizations. If you're buying hardware today, don't wait. CodeLlama 34B and Llama 2 34B are stable, well-tested, and widely used. They'll be relevant for another 18–24 months.

If you want the absolute newest release, the Llama 3.1 8B runs on anything with 8GB VRAM. The 70B is where the intelligence jump happens, but you need the hardware to match.

FAQ

Can I run Llama 34B on my gaming GPU? Depends which one. RTX 4090? Yes, easily. RTX 4070? Only with CPU offloading, which is slow. RTX 3090? Yes, perfectly. Check your card's VRAM — 24GB is comfortable, 16GB is tight, 12GB requires heavy offloading. If you don't know, run nvidia-smi and look at the memory total.

Is Q3_K_M or Q4_K_M better? Q4_K_M is more intelligent and catches more nuance. Q3_K_M is noticeably faster and uses 6GB less VRAM. For coding (CodeLlama's strength), Q4_K_M is worth the extra VRAM if you have it. For general chat, Q3_K_M is fine. Start with Q4_K_M; if your card runs out of memory, switch to Q3_K_M.

Will adding more system RAM speed up offloaded layers? No. Offloaded layers run on CPU, so they're limited by your CPU's speed and the PCIe bus bandwidth. More RAM just means they fit. Faster DDR5 helps more than more capacity, but it's a small gain (20–25%).

Llama 2 34B or CodeLlama 34B — which should I download? CodeLlama 34B if you write code. Llama 2 34B if you want general chat. CodeLlama was specifically optimized for code understanding; Llama 2 is the base model. Both are 34B and have the same hardware requirements. For AI-assisted coding or shell scripts, CodeLlama wins.

Can I run this on an M4 Mac with 36GB unified memory? Technically, but it's slow. Apple Silicon's unified memory architecture doesn't match NVIDIA's optimization for llama.cpp. Expect 5–8 tok/s, which is workable for low-frequency use cases. The Mac is better suited for Llama 3.1 8B or smaller models. If you already own the Mac, try it. If you're buying, go NVIDIA for 34B.

What's the power consumption of running CodeLlama 34B all day? RTX 3090 at 420W, 24/7 = ~100 kWh/month. RTX 4070 Ti Super at 285W = 68 kWh/month. At US average rates ($0.14/kWh), that's $14/month vs $9.50/month. The efficiency gap matters if this is your daily driver.

Last verified: March 30, 2026. CodeLlama 34B Q4_K_M GGUF size confirmed at 20.2GB. RTX 3090 used pricing verified across eBay and GPU reseller listings. RTX 4070 Ti Super new price verified at major retailers.

Sources:

• Meta Llama 3.1 official announcement
• HuggingFace TheBloke CodeLlama 34B GGUF repository
• Hardware-Corner GPU ranking for LLM inference
• CraftRigs tested inference speeds — RTX 3090 vs 4070 Ti Super CodeLlama 34B week-long trial