GPU Memory Bandwidth Buying Guide for Local LLM Inference

# GPU Memory Bandwidth Buying Guide for Local LLM Inference

*Kaito Tanaka*

Bottom line: buy enough VRAM first, then maximize memory bandwidth within a defensible checkout price and power envelope. The RTX 5090 is the strongest single-card option here for fully offloaded models that fit inside 32GB, but its observed pricing is too distorted for an unconditional recommendation. The RTX 5080 supplies high bandwidth but remains limited to 16GB; the RTX 4090’s 24GB makes a used example potentially more useful than a 16GB card; and the RX 9070 XT is the budget-oriented choice only for buyers prepared to manage backend-dependent performance.

Why Memory Bandwidth Matters for Local LLMs

Autoregressive decoding generates one token at a time while repeatedly streaming quantized model weights. When an otherwise identical model is fully offloaded to the GPU, memory bandwidth becomes a first-order ceiling on single-stream token generation. More bandwidth lets the GPU move those weights faster during each decoding step.

That does not make bandwidth a complete performance specification. Real throughput also depends on kernel quality, quantization format, model architecture, context length and KV cache, clocks and power limits, drivers, backend, and the host system. A GPU with greater quoted bandwidth can therefore underperform expectations when software support or a particular execution path is weaker.

The other hard constraint is VRAM capacity. Bandwidth affects how quickly a fitting workload can run; VRAM determines whether the weights and KV cache fit at all. If they do not, partial offload or another compromise changes the performance regime and invalidates a simple bandwidth comparison.

Practical capacity expectations from the available evidence are:

• 16GB: appropriate for 7B/8B Q4 models and selective 14B workloads, subject to file size and context.
• 24GB: provides more workable room for models in the 20B–32B Q4 range, though fit remains configuration-dependent.
• 32GB: offers the most flexibility among these cards for 20B–32B Q4-class workloads and larger contexts, but still requires checking the actual file and KV-cache demand.
• 70B Q4: do not assume that it fits in 32GB. This guide makes no such recommendation.

Capacity outranks theoretical speed when the model cannot remain on the GPU. A fast 16GB card is not a substitute for 24GB or 32GB when the intended model, quantization, and context exceed its usable memory.

Verified Hardware Comparison

The following table uses launch MSRP rather than volatile street prices. Specifications come from the official pages for the RTX 5090↗, RTX 5080↗, RX 9070 XT↗, and RTX 4090↗.

| GPU | VRAM | Bandwidth (GB/s) | Board power (W) | Launch MSRP | Capacity position | |---|---:|---:|---:|---:|---| | RTX 5090 | 32GB | 1,792 | 575 | $1,999 | Best here | | RTX 4090 | 24GB | 1,008 | 450 | $1,599 | Middle | | RTX 5080 | 16GB | 960 | 360 | $999 | Entry | | RX 9070 XT | 16GB | 640 | 304 | $599 | Entry |

The RTX 5090 combines the largest VRAM pool and highest bandwidth in this four-card comparison. Its drawback is equally clear: 575W total graphics power makes system power delivery and cooling central buying criteria, not afterthoughts.

The RTX 5080 reaches 960GB/s, close to the RTX 4090’s computed 1,008GB/s, but its 16GB capacity is materially more restrictive than the 4090’s 24GB. The RX 9070 XT has the lowest bandwidth at 640GB/s and the lowest listed board power at 304W, while sharing the same 16GB capacity class as the RTX 5080.

Capacity Fit Before Bandwidth

For 7B/8B Q4 inference, all four GPUs are practical capacity candidates. The decision can therefore emphasize measured throughput, backend support, price, and power.

For selective 14B workloads, both 16GB cards may work, but the exact quantized file and context must be checked before purchase. This is not a blanket assurance that every 14B configuration fits comfortably.

For 20B–32B Q4-class use, 24GB and 32GB are the workable capacity categories identified in the evidence. The RTX 4090 and RTX 5090 therefore deserve priority over nominally newer but smaller 16GB choices. Model fit is configuration-specific, especially as context and KV-cache requirements increase.

Interpreting Token-Generation Results

The project’s llama-bench documentation↗ defines `tg` as token generation measured in tokens per second. Results should be compared only when the model, quantization, backend, context, and other settings are identical.

The approximate figures below are aggregated llama.cpp-like `tg128` records for Llama 2 7B Q4_0, drawn from the CUDA, ROCm, and Vulkan scoreboard↗. They are not measurements from one controlled laboratory platform.

| GPU and backend | Approx. tg128 (t/s) | |---|---:| | RTX 5090, CUDA | 290–300 | | RTX 4090, CUDA | 188 | | RTX 5080, CUDA | 182 | | RX 9070 XT, Vulkan | 137 | | RX 9070 XT, ROCm | 101 |

Under this specific workload, the RTX 5090 produces the highest aggregated result. The RTX 4090 and RTX 5080 records are close despite their different memory generations and capacities. The RX 9070 XT’s Vulkan record exceeds its ROCm record, directly illustrating why backend selection can alter the buying outcome.

These numbers are conditioned observations, not a controlled GPU ranking. Differences can change with the model, quantization, backend, context, software revision, power behavior, and host configuration.

Why Bandwidth Does Not Predict the Table Exactly

A simple bandwidth ratio cannot reproduce every result because decoding is not performed by memory chips in isolation. Kernels must use the available bandwidth effectively, and backend maturity can determine whether the hardware approaches its theoretical position.

The AMD results need particular care. The documented 137 t/s Vulkan and 101 t/s ROCm records are not grounds for declaring one backend universally superior. They show variability in the aggregated records, while the RX 9070 XT backend discussion↗ reinforces that software path matters. Buyers should validate the backend they actually intend to run.

RTX 5090 results also vary with model scale and configuration. Supplementary Qwen benchmark records↗ report approximately 200 t/s for Qwen3 8B, 124 t/s for 14B, and 61 t/s for dense 32B, with varying configurations. These records demonstrate why “tokens per second” is meaningless without the attached model and setup.

MSRP Versus the 2026 Street Market

Launch MSRP is useful for understanding product positioning, but it is not a current-price claim. The street observations below were checked on October 21, 2026, and some listings were clearly unsuitable as representative retail pricing.

Observed October 21, 2026 Pricing

• RTX 5090: $1,999 launch MSRP. The check ranged from a $1,999 Bonanza marketplace listing to mainstream AIB listings at $4,766–$6,579. Describe the snapshot as “from a $1,999 marketplace listing, but mainstream AIB listings observed at $4,766+,” not as a normal retail range.
• RTX 5080: $999 launch MSRP. Observed new-card listings were $1,799.99 at Central Computers, $1,951.81 at Compu Devices, and $2,374.99 at Techinn: a street snapshot of approximately $1,800–$2,375.
• RX 9070 XT: $599 launch MSRP. No clean standalone quote from a reputable current US retailer surfaced, so $599 is only the price baseline, not a current street price.
• RTX 4090: $1,599 launch MSRP. New stock was scarce, with marketplace or business listings at $4,849–$5,199. These should not be treated as typical; the used market is the practical channel.

A live retailer search such as this RTX 5090 price-check page↗ should be treated only as a checkout-time availability check. Search snippets, marketplace offers, and inflated discontinued stock are not equivalent to a reliable in-stock card sold directly by a mainstream retailer.

Pricing integrity changes the verdict. An RTX 5090 may be the performance leader, but a purchase at $4,766 or more is a radically different decision from one near its $1,999 MSRP. Likewise, an RTX 5080 at $1,800–$2,375 cannot be evaluated as though it still costs $999.

Recommendations by Shopper Class

Maximum Single-GPU Throughput and 32GB Capacity

Choose the RTX 5090 if your workload fits within 32GB, you want the highest observed Llama 2 7B Q4_0 generation result in this comparison, and your system can support a 575W GPU. It is the strongest technical choice here for 20B–32B Q4-class workloads when the actual files and context fit.

The recommendation is strictly conditional on checkout price. Distorted 2026 listings prevent a universal value verdict. Confirm the seller, warranty path, final price, power supply capability, chassis clearance, and cooling before committing.

CUDA Buyer Needing More Than 16GB

Consider a used RTX 4090 when 24GB is sufficient and the price is defensible. Its 1,008GB/s bandwidth and approximate 188 t/s conditioned result remain relevant, while its capacity can be more valuable than the RTX 5080’s newer 16GB configuration.

Avoid scarce “new” listings at $4,849–$5,199. At those observed prices, new old stock is not a sensible baseline. Condition, seller credibility, and actual used price determine whether the 4090 is practical.

CUDA Buyer Staying With 7B/8B and Selective 14B

The RTX 5080 suits a buyer who wants CUDA, expects workloads to remain within 16GB, and values 960GB/s bandwidth with a lower 360W TGP than the two larger NVIDIA cards. It is not the right purchase if 24GB or 32GB capacity is likely to become necessary.

Its observed $1,800–$2,375 street snapshot is the main problem. At that level, compare the final price directly with viable used RTX 4090 offers and any trustworthy RTX 5090 availability rather than relying on the $999 MSRP.

Budget-Focused Buyer Comfortable With Backend Work

The RX 9070 XT is the candidate for a shopper anchored to the $599 MSRP baseline, limited to 16GB-class workloads, and willing to test Vulkan and ROCm. Its 304W typical board power is the lowest listed value here.

This is not a plug-in substitute for CUDA assumptions. The recorded 137 t/s Vulkan versus 101 t/s ROCm spread makes backend validation part of the purchase decision. Because no clean reputable standalone US quote surfaced, verify a live price at checkout before making a value comparison.

Final Buying Checklist

Before ordering, verify all of the following:

• Model fit: quantized file size plus KV-cache demand at the intended context.
• Backend fit: CUDA, Vulkan, or ROCm support for the software you will actually use.
• Benchmark fit: same model, quantization, context, backend, and generation test.
• Power fit: 575W, 450W, 360W, or 304W GPU demand, plus an appropriate system design.
• Cooling fit: sufficient chassis airflow and physical accommodation for the selected board.
• Price integrity: final checkout price, seller type, stock status, and whether the card is new or used.

The correct local-LLM GPU is therefore not automatically the card with the largest bandwidth number. It is the card that fits the workload in VRAM, converts bandwidth into throughput through a suitable backend, fits the system’s power and cooling envelope, and remains rational at the actual checkout price.