There is no universal winner: use a supported BF16 checkpoint as your quality reference, try FP8 when your GPU and inference engine support the exact format, and consider INT4 when memory is the constraint or it lets you run a meaningfully larger model. Then test the same agent tasks on each candidate. A lower bit width can change memory use, output quality, latency, and whether the checkpoint loads at all.
What BF16, FP8, and INT4 actually describe
These labels refer to numerical formats, but they do not by themselves tell you which parts of a model use that format. Check whether a setup quantizes weights, activations, or both, and inspect the checkpoint’s quantization recipe and runtime requirements.
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| Format or path | What it means | What to check |
|---|---|---|
| BF16 | 16-bit floating point, often used as a higher-precision inference baseline. | Confirm that both weights and activations run in BF16 if that is the comparison you intend; “BF16” alone can leave the configuration underspecified. |
| FP8 | 8-bit floating point. Implementations can differ in FP8 representation, scaling, and whether weights, activations, or both are quantized. | Identify the exact FP8 scheme and supported kernels. For example, vLLM documents a W8A8 FP8 path, while NVIDIA TensorRT documentation distinguishes per-tensor or per-axis FP8 from block-scaled MXFP8. |
| INT4 | 4-bit integer quantization, commonly used for weight-only quantization. | Weight-only INT4 stores weights at 4 bits while inputs and computation remain at higher precision. That is not equivalent to quantizing weights and activations together. |
In names such as W8A8 or W8A16, W refers to weight precision and A to activation precision. The letters are useful because two checkpoints both described casually as “8-bit” may take different execution paths and have different quality or hardware requirements.
Why a format change can break a local agent
The GPU or kernel path may not support it
Format support depends on GPU generation, inference engine, implementation, and quantization path. As one version-specific example, vLLM documentation describes W8A8 FP8 support for Hopper and Ada Lovelace GPUs, and W8A16 on Ampere using Marlin kernels. That is a statement about the documented vLLM implementation, not a guarantee for every engine or checkpoint.
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The checkpoint and runtime may disagree
A checkpoint may expect a particular quantization recipe, scaling scheme, or engine handling. Before treating a load error as a generic FP8 or INT4 problem, check the model card and quantization metadata, then verify the engine version and its documented support for that format. Quantization schemes and Q/DQ handling differ across implementations, so support for a format name alone does not establish that a specific checkpoint and runtime are compatible.
Weight savings do not remove every memory limit
Quantizing weights can free memory, but total serving memory also includes the KV cache, runtime buffers, and any concurrent requests. Longer context and more concurrency can increase those other demands. A lower-bit checkpoint may fit when a higher-precision one does not, but bit width alone cannot guarantee that a model, context length, and workload will fit a particular GPU.
Agent quality is task-specific
Small changes in model outputs can matter disproportionately when an agent must call the right tool, produce valid structured output, retrieve evidence, write code, reason through a task, or refuse appropriately. A broad benchmark can provide context, but it cannot establish that your own prompt, tool definitions, and workload still work.
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Fewer bits do not automatically mean lower latency
Latency and throughput depend on kernels, hardware, model size, batch size, and workload. Interactive single-user use is not the same as batched serving. The Association for Computational Linguistics study reports workload-dependent deployment recommendations, and the batch-dependent results in NVIDIA Model Optimizer’s benchmark illustrate why one throughput figure should not be generalized to every local setup.
What published results do—and do not—show
The Association for Computational Linguistics’ 2025 study reports more than 500,000 evaluations of Llama 3.1-family models across academic and real-world tasks. Under that study’s methods and evaluation setup, it found FP8 W8A8 effectively lossless across the tested model scales and benchmarks. It also reports 1–3% average accuracy degradation for well-tuned INT8 W8A8. These findings are evidence about the tested methods and tasks, not a promise for every checkpoint, quantization recipe, or agent.
NVIDIA Model Optimizer reports the following Llama 3.1-8B throughput results using an H100, Model Optimizer v0.21.1, and TensorRT-LLM v0.15. The figures are vendor-reported benchmark results, not measurements of a local-agent workload:
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| Batch size | BF16 (tokens/sec) | FP8 (tokens/sec) | INT4 AWQ (tokens/sec) |
|---|---|---|---|
| 1 | 173.80 | 245.03 | 231.75 |
| 8 | 803.11 | 1,051.17 | 599.72 |
The same NVIDIA benchmark reports MMLU loss relative to BF16 for Llama 3.1 instruct models on an H100 with Model Optimizer v0.21.1 and TensorRT-LLM v0.15:
| Model | FP8 MMLU loss | INT4 AWQ MMLU loss |
|---|---|---|
| Llama 3.1-8B instruct | 1.50% | 5.66% |
| Llama 3.1-70B instruct | 0.38% | 1.07% |
Those quality figures use MMLU and should not be read as a direct estimate of tool-call success, coding quality, or structured-output reliability. Likewise, the throughput table does not establish interactive latency or performance on other hardware and engines.
vLLM documentation claims supported FP8 quantization can provide 2x lower model memory requirements and up to 1.6x throughput improvement. Those are project documentation claims; actual results depend on the hardware and path used. Model-memory reductions are not the same as a guarantee about total VRAM use once KV cache and runtime overhead are included.
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Choose a format for the constraint you actually have
Start with BF16 when quality is the reference point
For a single-user interactive setup, start with a supported BF16 checkpoint if it fits. It provides a practical reference for checking whether a quantized candidate changes the agent’s behavior. If it does not fit, record which constraint is binding—weights, context, or concurrency—rather than assuming the format label alone explains the memory requirement.
Try FP8 when the implementation matches
FP8 is a reasonable next candidate when the GPU, engine, kernels, and checkpoint support the same FP8 scheme. vLLM documents dynamic conversion from an original BF16 or FP16 model to FP8 without calibration data; runtime conversion and a pre-quantized checkpoint are different deployment choices and may have different performance characteristics. Verify the specific engine path rather than assuming every FP8 artifact behaves alike.
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Consider INT4 when memory is the binding constraint or the reduced weight footprint lets you run a meaningfully larger model. Compare specific recipes and checkpoints: “INT4” does not describe all scaling, grouping, or implementation choices. NVIDIA’s TensorRT documentation warns that aggressive quantization can reduce accuracy because quantization introduces error.
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How to evaluate candidates on your agent
Keep the comparison controlled. Change the checkpoint or quantization path, not the task setup, so a change in results can be interpreted.
- Verify compatibility. Record the GPU, inference engine and version, checkpoint format, quantization metadata, and supported kernel path. Resolve load errors before comparing answer quality.
- Fix the workload. Use the same prompts, system instructions, tool definitions, sampling settings, context length, and concurrency for each candidate. Include representative tasks such as tool selection, schema-constrained output, retrieval, coding, and refusal behavior where relevant to your agent.
- Measure task success. Score whether the agent completed the task, not only whether its answer looks plausible. Track tool-call correctness and structured-output validity where those matter, and inspect failures rather than relying only on an average score.
- Measure interactive performance. For local use, record time to first token and decode rate under the same context and session conditions. If you also care about serving multiple users, test realistic concurrency separately rather than treating batch throughput as a substitute.
- Measure memory under the same conditions. Observe peak GPU memory at the target context length and concurrency. Distinguish model weights from total serving memory, including KV cache and runtime use.
- Repeat enough to spot instability. Re-run tasks where generation variability matters, preserve the exact checkpoint and settings, and note whether the model consistently loads and completes the tool workflow.
NVIDIA’s local-AI model-selection guidance recommends defining VRAM and performance requirements and using a custom evaluation dataset with human evaluation. A GPU advertised with 24GB of VRAM is a hardware category, not proof that any particular model, context length, or agent workload will fit.
Diagnose a regression after switching formats
- The checkpoint fails to load: compare its quantization metadata with the engine’s supported formats, version, and kernels. Test a known-compatible artifact before concluding that the GPU cannot run the format.
- It loads but tools fail: inspect malformed arguments, missed tool calls, and schema violations on the same prompts used with the BF16 reference. This isolates agent behavior from unrelated changes to prompts or sampling.
- It runs out of memory later: check whether failure occurs as context grows or concurrency increases. Weight memory is only one part of serving memory; reduce the workload dimensions that exceed the available capacity or select a smaller footprint.
- It is slower than expected: check whether the intended quantized kernel is actually active, and compare at the same batch size, context, and workload. A format can reduce storage without improving the particular latency measure you care about.
Practical decision
Use BF16 as the quality reference when it fits; test a compatible FP8 path for a possible memory or speed advantage; choose INT4 when its memory savings solve a real constraint and its task performance remains acceptable. The right choice is the exact checkpoint and runtime combination that reliably completes your agent’s tasks within your memory and latency limits—not the smallest bit-width label.
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