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VRAM capacity determines whether a local language model’s weights, key-value (KV) cache, and runtime data can fit on the GPU. Memory bandwidth influences how quickly the GPU can read that data during output generation. Because token-by-token decoding is commonly memory-bound, bandwidth can strongly affect tokens per second—but it does not determine speed by itself.
Why can a model fit in VRAM and still generate slowly?
Fitting a model is a capacity question; generating quickly is also a data-delivery question. During autoregressive decoding, the GPU produces output one token at a time. It must repeatedly supply model weights and other data from memory, so the rate at which memory can feed computation can limit generation speed.
NVIDIA’s inference overview describes decode as commonly memory-bound, with transfers of weights, keys, values, and activations contributing heavily to latency. Its July 31, 2026 guidance identifies high-bandwidth memory (HBM) bandwidth as the primary decode bottleneck for the workload it analyzes. These are workload-dependent explanations, not a guarantee that a particular bandwidth figure produces a specific consumer GPU’s tokens-per-second rate.
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Capacity answers whether the working set can reside in GPU memory. The model weights are only part of that set: KV cache, activations, input/output tensors, and runtime buffers also need memory. TensorRT-LLM documents these contributors and explains that memory use varies with precision and parallelism in its memory usage documentation.
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For scale, NVIDIA gives an approximate example of about 14 GB for the weights of a 7-billion-parameter model loaded at FP16 or BF16. That is an illustrative weight calculation, not a complete VRAM budget. In a separate example, NVIDIA estimates about 2 GB of KV cache for Llama 2 7B at 16-bit precision, batch size 1, and sequence length 4,096. Cache requirements change with model architecture, precision, sequence length, and batch size.
Bandwidth, typically expressed in GB/s, describes how quickly memory can supply data. When decode is limited by data movement, more bandwidth can help the GPU produce tokens faster. But observed speed also depends on model size and architecture, quantization, context length, runtime and kernels, batching, and where the data resides.
Why context length and concurrency change memory needs
The KV cache stores attention-related keys and values from the sequence so the model can use them as it generates. It grows with sequence length and batch size. A model that fits for a short prompt and one request may therefore exceed available VRAM at a longer context or with multiple concurrent requests.
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A longer context can affect more than whether the workload fits: it can increase the amount of cache data involved in generation. If some working data cannot stay in GPU memory, its placement and transfers can affect performance. A useful capacity check must account for the intended model, precision, context, number of simultaneous requests, and runtime overhead—not just the model’s weight size.
Prompt processing and output generation are different workloads
Prefill processes the prompt
Prefill processes the prompt and computes intermediate attention states. Because it handles many prompt tokens in parallel, it is often compute-bound. Prompt-processing throughput describes this phase, not the rate at which the model writes its answer.
Decode generates the answer
Decode produces output one token at a time and is commonly memory-bound. The output generation rate is therefore especially sensitive to memory traffic. NVIDIA’s July 31, 2026 attention guidance also describes exceptions: speculative decoding can raise decode arithmetic intensity and shift the bottleneck toward compute, while prefix caching can make prefill for a short new prompt behave more like decode when it draws on a long cached sequence. “Often memory-bound” is more accurate than “always memory-bound.”
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What does tokens per second mean?
Check the metric before comparing figures. NVIDIA defines inter-token latency (ITL), also called time per output token, as the average interval between consecutive generated tokens; its AIPerf formula excludes time to first token. System tokens per second is aggregate output tokens divided by the benchmark interval from the first request to the final response. Under concurrent load, aggregate throughput can rise as requests are added until GPU compute resources saturate, and then fall.
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Per-user generation speed and aggregate system throughput are not interchangeable. A system serving several requests may produce many tokens per second in total even when each user receives tokens more slowly. Time to first token (TTFT), prompt throughput, per-user ITL, and aggregate throughput answer different questions. NVIDIA’s benchmarking metrics documentation defines these metrics and discusses comparison caveats.
How to compare local LLM performance fairly
Two tokens-per-second figures are useful to compare only when they describe sufficiently similar workloads. Align these details before drawing conclusions:
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- Model and tokenizer: Different tokenizers can divide the same text into different numbers of tokens, so tokens are not necessarily equivalent units of text.
- Prompt and output lengths: Prompt length affects prefill; generated length and context affect decode and cache traffic.
- Concurrency or batch size: These affect KV-cache needs and the distinction between per-user speed and aggregate throughput.
- Precision or quantization: These change memory use and can affect computation and performance.
- Runtime and kernel settings: Different software paths can produce different results on the same hardware.
- Metric definition: Separate prompt-processing rate, TTFT, per-user ITL, and aggregate system tokens per second.
NVIDIA’s inference optimization overview covers the distinct inference phases and memory considerations. Its long-context attention guidance explains workload-specific bottlenecks and exceptions. Together, these sources are useful for understanding why a benchmark result should not be generalized without its workload settings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the bandwidth examples do—and do not—show
NVIDIA reports 900 GB/s of total CPU–GPU NVLink-C2C bandwidth on GH200, describing it as seven times the bandwidth of standard PCIe Gen5 lanes in traditional x86-based GPU servers. This is a platform-interface comparison, not a consumer graphics card’s memory-bandwidth specification.
In a described GH200-versus-x86-H100 Llama 3 70B multiturn scenario, NVIDIA reports up to 2× faster time to first token. That vendor result concerns KV-cache offloading and TTFT; it does not establish that GH200 generally doubles decode tokens per second. See NVIDIA’s GH200 multiturn inference explanation for the scenario and claim.
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How to use capacity and bandwidth when choosing hardware
Start with the workload you intend to run rather than a single headline specification. Check whether the model at your chosen precision can fit alongside its expected KV cache and runtime memory at your target context length and concurrency. Then compare memory bandwidth and measured performance for the same model and workload, distinguishing prompt processing from output decoding.
Also account for power, system compatibility, and cost using current product-specific information. The sources cited here do not rank consumer graphics cards, establish current prices, or provide an apples-to-apples consumer GPU benchmark for this topic. No single bandwidth number can substitute for a workload-matched measurement.
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