Deploy GPU ↗
HARDWARE FIT CHECK • Meta Llama 3.3 70B Instruct + AMD Radeon RX 7900 XTX (24GB GDDR6)49.96 GB / 24 GB VRAM

Can I Run Meta Llama 3.3 70B Instruct on AMD Radeon RX 7900 XTX (24GB GDDR6)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Meta Llama 3.3 70B Instruct (70.6B) on AMD Radeon RX 7900 XTX (24GB GDDR6) (24 GB).

✕ OOM49.96 GB (208%)0 t/s

Model & Target Hardware

interactive
Standard 70B Dense Enterprise Baseline for 48GB–64GB Rigs
GPU Topology / Quantity:Single GPU / Unified SoC
Meta Llama 3.3 70B Instruct on AMD Radeon RX 7900 XTX (24GB GDDR6)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM
Required VRAM
49.96 GB
208% of hardware capacity (24 GB)
Est. Decode Speed
0 tok/s
Dense model @ 960 GB/s
Available Headroom
-25.96 GB
Needs +25.96 GB more VRAM
Memory Allocation & Headroom
49.96 GB allocated of 24 GB capacity
208% utilized
Weights: 42.36 GB
KV: (32K): 5.37 GB
Overhead: 2.23 GB
Deficit: -25.96 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on AMD Radeon RX 7900 XTX (24GB GDDR6) (960 GB/s). Prompt prefill / TTFT (~0 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run llama3.3:70b

Local Hardware Configurations & Pricing

⚠️ Out of VRAM: High-Capacity Hardware Upgrades
High-bandwidth VRAM specs and current market prices
Multi-GPU Rig↑ Upgrade
48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8

936 GB/s700W TDP
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Apple Silicon✓ Fits Model
112 GB VRAM

Apple Mac Studio M4 Max (128GB Unified Memory)

Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context

546 GB/s145W TDP
$3,499Amazon Prime →
Consumer GPU
24 GB VRAM

AMD Radeon RX 7900 XTX (24GB GDDR6)

Cheapest 24GB brand-new GPU for ROCm / Ollama on Linux

960 GB/s355W TDP
$899Amazon Prime →

⚠️ Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Meta Llama 3.3 70B Instruct @ 49.96 GB VRAM)

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$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)
Launch 1-Click GPU Pod →
Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)
Rent Spot GPU →
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$1.99/hr (GH200 / H100)
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Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw141.2 GB5.37 GB152.26 GB (634%)0 t/sOOM
Q8_08.5 bpw74.84 GB5.37 GB83.58 GB (348%)0 t/sOOM
Q6_K6.56 bpw57.89 GB5.37 GB66.04 GB (275%)0 t/sOOM
Q5_K_M5.68 bpw50.13 GB5.37 GB58 GB (242%)0 t/sOOM
Q4_K_M4.8 bpw42.36 GB5.37 GB49.96 GB (208%)0 t/sOOM
FP44.25 bpw37.42 GB5.37 GB44.85 GB (187%)0 t/sOOM
IQ3_XS3.3 bpw28.95 GB5.37 GB36.08 GB (150%)0 t/sOOM

Export Full Architecture & Infrastructure Spec

Get an executive-ready PDF report with exact memory math, hardware benchmarks, and cost comparisons.

FAQ & Reference

Frequently Asked Questions: Meta Llama 3.3 70B Instruct on AMD Radeon RX 7900 XTX (24GB GDDR6)

How much VRAM does Meta Llama 3.3 70B Instruct need on AMD Radeon RX 7900 XTX (24GB GDDR6)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Meta Llama 3.3 70B Instruct requires 49.96 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Meta Llama 3.3 70B Instruct achieve on AMD Radeon RX 7900 XTX (24GB GDDR6)?

Based on AMD Radeon RX 7900 XTX (24GB GDDR6)'s 960 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.