VLLM JSON Output Malformed After CUDA Upgrade To 12.4

Q: Reproducing the failure locally

Run a single‑request test with the same model and response_format='json_object':

Q: GPU memory pressure check

Monitor nvidia-smi during the request:

Q: Resolution Patch the output parser to guard against incomplete streams

Modify vllm/engine/outputs.py to validate the JSON buffer before emitting it. The change adds a final “try‑parse” step and, on failure, waits for the next token batch up to a short timeout.

Q: Adjust token limits and response format

Explicitly set a generous max_new_tokens that exceeds the expected JSON length and add a stop token that guarantees closure.

Q: Disable the aggressive torch.compile path for CUDA 12.4

vLLM exposes the flag via the environment variable VLLM_DISABLE_TORCH_COMPILE=1. Disabling it removes the kernel‑cache error that leads to early termination.

Problem Description

Symptoms and Impact

After upgrading the Docker image to CUDA 12.4, vLLM v0.6.3 started emitting malformed JSON for function‑calling prompts. The downstream FastAPI gateway receives a truncated or syntactically invalid JSON payload, which triggers json.JSONDecodeError exceptions and causes request drops.

Log snippet from a worker thread:

INFO vllm.engine.output_parser - Received partial JSON: {"name": "search", "parameters": {
ERROR json.JSONDecodeError: Expecting ',' delimiter (char 58)

FastAPI validation error:

FastAPI ValidationError: field required (type=value_error.missing)

GPU memory spikes to ~95 % right before the failure, as shown in CloudWatch metrics.
Concurrent requests (≥ 20) increase the frequency of the issue.

Operationally the problem manifests as:

Interrupted agentic workflows.
Increased latency due to retries.
Spurious “tool use parsing failure” warnings in vllm.engine.output_parser.

Root Cause Analysis

Why the JSON becomes malformed

vLLM streams model tokens directly to the HTTP response. The streaming buffer is sized based on the CUDA runtime’s tensor serialization path. CUDA 12.4 introduced changes in libcusparse and libcuda that affect the internal torch.compile kernel cache. When the driver version mismatches the PyTorch build bundled with vLLM, the following sequence occurs:

The torch.compile flag (enabled by default in v0.6.x) triggers a just‑in‑time compilation step that, under CUDA 12.4, can raise RuntimeError: CUDA error: unknown error once GPU memory exceeds ~90 %.
The runtime error aborts the current token generation loop. vLLM’s output parser receives the token batch that was already flushed to the client, but the closing brace } of the JSON object has not been generated yet.
Because the parser treats the partial buffer as a complete response (it cannot differentiate an aborted stream from a finished one), it forwards the incomplete string to FastAPI, which then raises json.JSONDecodeError.

This behavior aligns with the known issue reported in the vLLM 0.6.x release notes (“known issues with token truncation after CUDA driver upgrades”) and the GitHub issue #3125 where users observed identical truncation patterns after moving to CUDA 12.4.

Investigation and Debugging

Log analysis

Collect the worker logs around the failure time. The following pattern is typical:

2024-08-15 12:03:41,872 WARN vllm.engine.output_parser - Truncated response due to token limit
2024-08-15 12:03:41,873 ERROR vllm.engine.worker - RuntimeError: CUDA error: unknown error
2024-08-15 12:03:41,874 INFO vllm.engine.output_parser - Received partial JSON: {"name": "search", "parameters": {

Reproducing the failure locally

Run a single‑request test with the same model and response_format='json_object':

curl -X POST http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
        "model": "meta-llama/Meta-Llama-3-8B-Instruct",
        "messages": [{"role":"user","content":"Call the function search(query=\"openAI\")"}],
        "response_format": {"type":"json_object"}
      }' | python -m json.tool

Under CUDA 12.4 the command returns:

json.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

GPU memory pressure check

Monitor nvidia-smi during the request:

# nvidia-smi --query-gpu=memory.used,memory.total,utilization.gpu --format=csv -l 1
memory.used [MiB], memory.total [MiB], utilization.gpu [%]
  15872 MiB,  16384 MiB, 94 %

The spike coincides with the runtime error, confirming the memory‑pressure hypothesis.

Confirming the CUDA‑PyTorch incompatibility

Check the PyTorch build version inside the container:

python -c "import torch, sys; print(torch.__version__, torch.version.cuda)"
# Output: 2.3.0+cu124 12.4

Even though the binary reports cu124, the underlying driver on the EC2 instance (NVIDIA driver 525.x) is compiled against CUDA 12.2, leading to subtle mismatches that surface only under high memory load.

Resolution

Patch the output parser to guard against incomplete streams

Modify vllm/engine/outputs.py to validate the JSON buffer before emitting it. The change adds a final “try‑parse” step and, on failure, waits for the next token batch up to a short timeout.

Before:

def parse_output(self, raw_text: str) -> str:
    # Directly forward whatever has been streamed
    return raw_text

After:

import json, time

def parse_output(self, raw_text: str) -> str:
    # Attempt to parse; if it fails, keep the buffer alive for a short grace period
    deadline = time.time() + 0.2  # 200 ms grace
    while time.time() < deadline:
        try:
            json.loads(raw_text)
            return raw_text
        except json.JSONDecodeError:
            # Pull next token batch from the stream (implementation‑specific)
            raw_text += self._read_next_chunk()
    # If still invalid, raise a clear error instead of sending broken JSON
    raise ValueError("Incomplete JSON payload from vLLM worker")

Adjust token limits and response format

Explicitly set a generous max_new_tokens that exceeds the expected JSON length and add a stop token that guarantees closure.

# FastAPI request payload
payload = {
    "model": "meta-llama/Meta-Llama-3-8B-Instruct",
    "messages": [{"role":"user","content":"Call function foo()"}],
    "max_new_tokens": 256,
    "stop": ["}"],               # ensure closing brace is emitted
    "response_format": {"type":"json_object"}
}

Disable the aggressive `torch.compile` path for CUDA 12.4

vLLM exposes the flag via the environment variable VLLM_DISABLE_TORCH_COMPILE=1. Disabling it removes the kernel‑cache error that leads to early termination.

# Dockerfile snippet
ENV VLLM_DISABLE_TORCH_COMPILE=1

Pin a compatible CUDA driver or downgrade the toolkit

If the host can be upgraded, install the NVIDIA driver version 525.105.17 (compiled against CUDA 12.4). Alternatively, rebuild the container with CUDA 12.2 to match the driver already present on the EC2 instance.

Validation

Functional verification

Run the same curl command after applying the fixes. The output should now be a well‑formed JSON object:

{
  "name": "search",
  "parameters": {
    "query": "openAI"
  }
}

Running python -m json.tool on the response should succeed without errors.

Load testing

Execute a concurrency test with hey or locust targeting 30 simultaneous requests:

hey -c 30 -n 300 -m POST -H "Content-Type: application/json" \
  -d payload.json http://localhost:8000/v1/completions

Observe that json.JSONDecodeError no longer appears in the FastAPI logs and that GPU memory utilization stays below 85 % throughout the run.

Operational Best Practices

Monitoring and alerting

Metric	Threshold	Alert
GPU memory utilization	> 90 %	High memory pressure – investigate token limits.
vllm.engine.output_parser WARN count	> 5/min	Potential JSON truncation – check CUDA driver compatibility.
FastAPI ValidationError rate	> 1 % of requests	Downstream parsing failure – verify response_format.

Container configuration recommendations

Set VLLM_DISABLE_TORCH_COMPILE=1 in production unless you have verified driver‑toolkit parity.
Allocate at least 2 GiB of GPU memory headroom; use max_gpu_memory_utilization=0.85 in the vLLM launch command.
Enable the built‑in JSON validator middleware from the FastAPI example repository to catch malformed payloads early.

Related Topic Hub: Model Serving Troubleshooting Hub

FAQ

Why does the error appear only after the CUDA upgrade?
The CUDA 12.4 runtime changes break the interaction between torch.compile and vLLM’s token streaming buffer, causing premature termination when GPU memory is high.
Can I keep torch.compile enabled?
Yes, but only after upgrading the host driver to a version built against CUDA 12.4 (e.g., NVIDIA driver 525.105.17) and confirming that memory utilization stays below the driver’s safety margin.
Is increasing max_new_tokens sufficient?
It mitigates the symptom but does not address the underlying runtime error. The proper fix is to disable the faulty compilation path or align driver/toolkit versions.
Do I need to change the model or the prompt?
No. The issue is independent of the model; it is triggered by the streaming implementation under specific CUDA conditions.
How can I detect incomplete JSON before it reaches FastAPI?
Enable the optional response_format='json_object' and wrap the vLLM client call with a try‑except block that validates json.loads() on the raw string; log a warning and request a retry if validation fails.