GPU for Local LLM: RTX 4090 vs 5090 vs Mac Studio vs A5000 vs 7900 XTX

The GPU you pick for local LLM inference determines two things: which model you can run without splitting weights to CPU RAM, and how fast responses arrive. This page maps five current hardware options across the specs that actually matter — VRAM, memory bandwidth, power, and real-world tokens-per-second — so you can match hardware to your model tier and workload before spending.

All token-per-second figures are single-user decode throughput on small (7–8B) models at Q4_K_M quantization via llama.cpp unless noted. Batch/vLLM throughput is a different metric and scales differently. Prices are approximate retail or secondary-market ranges as observed in mid-2026; verify at time of purchase. D-Central Technologies ships custom local-AI workstations to Canadian customers — see the build recommendations at the end of this page.

GPU comparison: VRAM, bandwidth, power, and throughput at a glance

Token-per-second figures are approximate single-user decode throughput (generation speed after the prompt); prefill/prompt-processing rates are 10–100× higher and not the relevant metric for user-facing latency. All figures vary with context window length, quantization tool, OS, driver version, and concurrent load. Sources: RTX 5090 community benchmarks including Spheron (May 2026) and OpenBenchmarking.org; RTX 4090 community consensus from llama.cpp GitHub discussions; Radeon RX 7900 XTX: 1337hero ROCm benchmark repository; Mac Studio M4 Max: Markaicode llama.cpp M4 Max benchmark (Oct 2025); M4 Ultra throughput extrapolated from bandwidth scaling — verify independently. All prices approximate, mid-2026; verify at time of purchase.

Which model sizes fit on each GPU?

The dominant constraint for local inference is VRAM, not compute. If the model weights do not fit in GPU memory (with overhead for KV cache and runtime), performance collapses due to CPU offload, or the model simply will not load. Use the table below to match model tier to hardware. Figures use Q4_K_M quantization as the practical default; add ~10–20% headroom for KV cache at normal context lengths.

Approximate VRAM figures for Q4_K_M (weights only). Real memory usage at runtime includes KV cache, which scales with context window length and concurrent requests — add 10–30% headroom in practice. MoE models (Llama 4 Scout, Qwen3-MoE, DeepSeek V4 Pro) require all expert weights to remain resident in memory, so the active-parameter count does not reduce the memory footprint.

GPU-by-GPU analysis

RTX 5090 — fastest consumer GPU, 32 GB ceiling

NVIDIA’s Blackwell-generation RTX 5090 posts the highest raw inference throughput of any single consumer GPU available as of mid-2026. Its 1,792 GB/s memory bandwidth — 77% higher than the RTX 4090’s 1,008 GB/s — directly translates to faster token generation, since decode-phase LLM inference is almost entirely memory-bandwidth bound. Community benchmarks report approximately 186 tokens/s on Qwen3 8B Q4_K_M via llama.cpp (source: Spheron benchmark series, May 2026; OpenBenchmarking.org 2025 run); the RTX 4090 achieves roughly 28–67% fewer tokens per second across model sizes in the same comparisons.

The 32 GB GDDR7 capacity is an 8 GB step up from the 4090, which matters specifically for the 27–33B model tier: a Qwen3 32B at Q4_K_M (approximately 20 GB weights) fits comfortably at 32 GB but is tight at 24 GB once KV cache is added. Beyond 32B, no discrete GPU in this comparison closes the gap — the 70B barrier requires 40+ GB regardless of which card you buy.

Who should buy it: Speed-first developers who want the fastest single-GPU decode for 8–32B models and are willing to pay the premium (and install the 575W power delivery it demands). Not meaningfully better than the 4090 for models under 20 GB; the gap opens on larger models where bandwidth advantage compounds.

RTX 4090 — the established benchmark, 24 GB standard

The RTX 4090 remains the community’s reference card for local LLM inference. Its 1,008 GB/s bandwidth (GDDR6X) delivers approximately 100–130 tokens/s on 7–8B models and 60–90 tokens/s on 13B models — widely verified across llama.cpp GitHub discussions, community leaderboards, and independent reviewers. It is the most-tested consumer GPU in the llama.cpp ecosystem; driver support, quantization compatibility, and edge-case debugging documentation are better established than for any alternative in this comparison.

The 24 GB ceiling means 70B models are out of reach without substantial CPU offloading (which slows decode by an order of magnitude). For organizations whose model needs fit below 27B, the 4090 is the price-performance optimum. Cost-per-million-tokens at single-user inference (all-in hardware amortization) generally favours the 4090 over the 5090 unless you specifically need the extra 8 GB or the bandwidth for very high-throughput workloads.

Who should buy it: Developers and small teams working in the 7–27B model range who want maximum llama.cpp ecosystem compatibility and the best-documented inference stack.

Mac Studio (unified memory) — model capacity and silence, not raw bandwidth

The Mac Studio occupies a unique category in this comparison. Its unified memory architecture — where the CPU and GPU share the same physical memory pool — means that a Mac Studio M4 Max configured with 128 GB can run a Llama 3.1 70B model at Q4_K_M quantization in full without any CPU offload. No discrete 24 GB or 32 GB GPU can do this. The M4 Ultra pushes this to 512 GB, enabling 400B-class models on a single desktop machine — a capability otherwise requiring a multi-GPU cluster.

The trade-off is bandwidth. The M4 Max’s 546 GB/s is approximately half the RTX 4090’s 1,008 GB/s. For small models (7–13B) that fit entirely in 24 GB discrete GPU memory, the Mac Studio delivers roughly 70–80 tokens/s versus the 4090’s 100–130 — a real and perceptible gap for single-user interactive use. For 70B models, however, the Mac Studio M4 Max 128 GB runs at approximately 12 tokens/s (source: markaicode.com llama.cpp M4 Max benchmark, October 2025) while the discrete 24 GB GPU would require such heavy offloading that it becomes unusable for interactive inference.

Mac Studio also draws approximately 37W (M4 Max) at typical load — versus 450W for the RTX 4090 system. Over a full year of 8-hour operation, that is a substantial electricity cost difference; Canadian users at provincial rates can calculate the delta using the province electricity rate reference. The Mac Studio is completely silent under normal inference load.

The preferred inference stack for Apple Silicon is Apple’s own MLX framework (open-source, developed by Apple Machine Learning) rather than llama.cpp, which is CUDA-first. llama.cpp does run on Apple Silicon via Metal, but MLX often delivers higher throughput on M-series hardware due to tighter platform integration. Both are open-source and free.

Who should buy it: Organizations that need to run 70B+ models on a single, silent desktop machine, particularly where noise levels, power draw, or data-jurisdiction requirements (running entirely on Canadian-owned hardware, off-cloud) are constraints.

Radeon RX 7900 XTX — competitive bandwidth, ROCm ecosystem caveat

The RX 7900 XTX (24 GB GDDR6, 960 GB/s) sits extremely close to the RTX 4090 on raw bandwidth (960 vs 1,008 GB/s — a 5% difference) and delivers competitive per-token inference speed. Community benchmark data from the 1337hero ROCm benchmark repository shows 119–131 tokens/s on Llama 7B Q4_0 (single GPU, ROCm, Flash Attention) — essentially matching the RTX 4090 range. Notebookcheck testing reported the RX 7900 XTX outperforming RTX 4090 and RTX 4080 Super in DeepSeek AI benchmarks, specifically the R1 reasoning workload.

The critical caveat is the software ecosystem. llama.cpp’s primary acceleration path is CUDA (NVIDIA); ROCm (AMD’s open-source GPU compute stack) is a second-class citizen in the majority of community tooling. ROCm support in llama.cpp has improved substantially and the HIP backend works for most inference workloads, but you may encounter: edge cases requiring ROCm-specific build flags, slower adoption of new llama.cpp features, reduced support in tools like Ollama, and occasional performance regressions noted in llama.cpp GitHub issue tracker (e.g., issue #20934). A Vulkan backend exists as a fallback but adds overhead. If you are comfortable building from source and debugging GPU driver issues on Linux, the 7900 XTX is a strong value proposition at its secondary-market price. On Windows, ROCm support is more limited.

Who should buy it: Cost-conscious Linux users comfortable with ROCm; open-source infrastructure advocates who prefer AMD’s fully open GPU compute stack over NVIDIA’s proprietary CUDA. Avoid if you want plug-and-play Ollama on Windows.

RTX A5000 — professional grade, not the fastest

The RTX A5000 is NVIDIA’s Ampere-generation professional workstation GPU (24 GB GDDR6, ECC, 230 W, NVLink support). It shares the same VRAM capacity as the RTX 4090 and 7900 XTX but has lower memory bandwidth (768 GB/s vs 1,008 GB/s) and an older compute architecture (Ampere, 2021 vs Ada Lovelace, 2022). For memory-bandwidth-bound LLM inference, throughput scales roughly with bandwidth: expect approximately 65–90 tokens/s on 7–8B Q4_K_M models, compared to 100–130 for the RTX 4090. Note: direct community llama.cpp benchmarks for the A5000 are sparse; this range is estimated from the bandwidth ratio and Ampere vs Ada efficiency factors — treat as indicative, verify with your own workload.

Where the A5000 earns its place is in always-on inference servers. ECC memory (error-correcting code) prevents silent data corruption over long-running inference sessions — relevant in production environments where a flipped bit in model weights could cause subtle output errors over days of operation. The A5000 is also workstation-certified for 24/7 operation with a MTBF designed for server contexts, ships with NVLink (two A5000s can be bridged for 48 GB effective VRAM using NVLink — enabling the 70B tier), and its professional drivers receive longer support cycles than consumer GeForce drivers. At 230 W TDP, it also draws roughly half the peak power of the RTX 4090.

Who should buy it: Organizations deploying inference as a persistent service (not a developer workstation) where ECC reliability and 24/7 uptime matter more than raw generation speed. Two A5000s with NVLink provides 48 GB for 70B models at competitive combined bandwidth. Used pricing in the $600–1,100 range makes it competitive as a reliability-first buy.

Why memory bandwidth governs local LLM speed

LLM text generation (the decode phase) is a memory-bandwidth-bound workload, not a compute-bound one. Each generated token requires reading the entire model weight tensor from VRAM — for a 7B Q4_K_M model (~5 GB), the GPU must transfer approximately 5 GB of weights per token. At 1,008 GB/s (RTX 4090), the theoretical maximum decode rate for that model is:

Real-world performance sits below this ceiling due to kernel launch overhead, KV cache reads, attention computation, and driver efficiency. Observed rates of 100–130 tok/s on 7B Q4_K_M for the RTX 4090 represent roughly 50–65% utilization of theoretical bandwidth — consistent with what the llama.cpp community reports as typical.

This bandwidth-bottleneck framing explains the table results directly: the RTX 5090 is faster than the 4090 because 1,792 GB/s ÷ 1,008 GB/s ≈ 1.78× more bandwidth, and observed throughput gains are 28–72%. It also explains why the Mac Studio M4 Max is slower on small models despite being powerful hardware: 546 GB/s is roughly half the 4090’s bandwidth. And it explains why the RTX A5000’s 768 GB/s puts it behind both the 4090 and 7900 XTX despite identical VRAM capacity.

Batch inference (multiple simultaneous requests) is different: with multiple concurrent prompts, GPU compute becomes a co-bottleneck alongside bandwidth, and NVIDIA’s CUDA-optimized path (particularly with vLLM’s continuous batching) tends to pull further ahead of AMD ROCm implementations. See the full local AI hardware guide for concurrency-based sizing.

Choosing hardware for Canadian sovereignty requirements

The hardware decision intersects with data-jurisdiction requirements in two ways that are especially relevant to Canadian organizations.

Power costs. GPU power draw has a real operating cost. At Quebec’s residential rate of approximately $0.073/kWh (as of 2025; verify via provincial rate reference), running an RTX 4090 (450 W) versus a Mac Studio M4 Max (37 W) for 8 hours/day produces annual electricity costs of approximately:

• RTX 4090: 0.45 kW × 8 h/day × 365 days × $0.073/kWh ≈ ~$96/year in electricity (Quebec) — just for the GPU
• Mac Studio M4 Max: 0.037 kW × 8 h × 365 × $0.073 ≈ ~$8/year

In Ontario (~$0.10/kWh) or British Columbia (~$0.115/kWh) these figures rise proportionally. Provinces with higher rates make the Mac Studio’s efficiency advantage more pronounced over a 3–5 year hardware lifecycle. Use the energy-for-compute reference for a fuller analysis of power economics at inference scale.

Data sovereignty. All five GPUs in this comparison support running models entirely on-premises with no cloud dependency — the core requirement for organizations processing sensitive Canadian data under Quebec Law 25, federal Privacy Act obligations, or financial-sector compliance mandates. The model weights run on your hardware; no query leaves your network. See Local LLM Canada and AI consulting Quebec for the full Canadian data-jurisdiction context.

Honest verdict: which GPU for which use case

Frequently asked questions

What is the best GPU for running Llama 3.1 70B locally?

No single discrete GPU in this comparison can hold Llama 3.1 70B at Q4_K_M quantization (~42 GB) without CPU offloading — the RTX 5090 (32 GB) and all 24 GB cards fall short. The practical options are: (1) Mac Studio M4 Max with 64 GB or 128 GB unified memory — runs 70B Q4 at approximately 12 tokens/s, silently, at low power; (2) two RTX A5000 GPUs linked via NVLink for 48 GB combined VRAM — fits 70B Q4_K_M with some headroom, with higher throughput than the Mac Studio on decode speed; or (3) a single H100 80 GB card (not in this comparison) — fits comfortably and delivers the best single-card throughput. VRAM figures are approximate and vary with context window settings.

Is the Radeon RX 7900 XTX good for local LLM inference?

Yes, with a Linux/ROCm qualification. The 7900 XTX’s 960 GB/s bandwidth is within 5% of the RTX 4090’s 1,008 GB/s, and community benchmarks confirm near-identical decode throughput for 7–27B models (119–131 tok/s on 7B Q4_0, per the 1337hero ROCm benchmark repository). The CUDA vs ROCm gap matters mainly at the ecosystem level: fewer tutorials, occasional build complications, and some llama.cpp features that take longer to reach ROCm. On a well-configured Linux system with a current ROCm release, the 7900 XTX is a strong value option. On Windows, CUDA is significantly more mature.

Why is the RTX A5000 slower than the RTX 4090 if they both have 24 GB VRAM?

VRAM capacity is only one axis. Memory bandwidth — the rate at which the GPU can read model weights from VRAM — directly governs token generation speed, since LLM decode is memory-bandwidth bound. The RTX A5000 has 768 GB/s bandwidth; the RTX 4090 has 1,008 GB/s — a 31% difference that produces a proportional gap in decode throughput. Additionally, the A5000 is an Ampere-generation card (2021) versus the 4090’s Ada Lovelace (2022), which also contributes. The A5000’s value is ECC memory, professional workstation certification, NVLink, and 230 W operation — not raw inference speed.

Does Apple Silicon (Mac Studio M4) really outperform NVIDIA GPUs for local LLM?

It depends on what you measure. For raw decode throughput on small models (7–13B), NVIDIA discrete GPUs at 24 GB+ are faster: the RTX 4090 delivers approximately 100–130 tok/s versus the Mac Studio M4 Max’s ~70–80 tok/s. Where Apple Silicon wins unambiguously is memory capacity for price: a Mac Studio M4 Max 128 GB enables 70B models no discrete 32 GB GPU can load, and a Mac Studio M4 Ultra 512 GB has no equivalent in the consumer or prosumer GPU market. For organizations whose primary need is to run large models efficiently at low power, Apple Silicon is genuinely the better choice. For fastest possible generation on 7–27B models, NVIDIA discrete GPUs lead on throughput per dollar.

Can I run two RTX 4090s together for more VRAM?

Yes, but with important caveats. Consumer GPUs including the RTX 4090 do not support NVLink (NVIDIA removed it after the Ampere consumer-GPU generation). Multi-GPU inference is possible via llama.cpp’s tensor-split feature (splitting layers across two GPUs connected only by PCIe), but PCIe bandwidth (64 GB/s in PCIe 4.0 ×16) becomes the cross-GPU bottleneck during inference. For a 70B model split across two RTX 4090s, effective throughput is significantly lower than two times a single card — the PCIe interconnect serializes the attention computation. The RTX A5000 supports NVLink for full bandwidth bridging across two cards, which is why the 2× A5000 NVLink configuration is more practical for 48 GB unified VRAM inference than 2× RTX 4090 PCIe split.

What about the RTX 4090 vs RTX 5090 for running Qwen3 32B?

Qwen3 32B at Q4_K_M requires approximately 20 GB of VRAM for weights. Both cards fit the model, but the RTX 5090 (32 GB) provides more room for KV cache — relevant for long-context work (coding, document analysis) where the KV cache grows with context length. The RTX 4090 (24 GB) leaves approximately 4 GB for KV cache after loading weights, which constrains usable context to roughly 4,000–8,000 tokens in practice depending on the model architecture. The RTX 5090 roughly doubles that headroom. Throughput on this model size is also higher on the 5090 due to bandwidth. If Qwen3 32B is your primary workload and long-context use matters, the 5090 is meaningfully better; for shorter contexts the 4090 covers the model at lower cost.

Related resources

• Local AI hardware guide — full tier map from Pleb AI Box to Hashcenter cluster
• Local LLM Canada — why running AI locally matters in Canada
• AI consulting Quebec — on-premises AI for Quebec organizations
• Sovereign AI Canada — the data-sovereignty case for local inference
• Distributed Compute — decentralized infrastructure beyond a single node
• AI Sovereignty Consulting — four-tier service from advisory to full hashcenter build-out
• Canada electricity rates by province — power cost reference for GPU workloads
• Energy for compute — power economics of local AI vs cloud
• D-Central Sovereign AI hardware — full product category